Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers
Authors: Wencong You
Zayd Hammoudeh
Daniel Lowd
Venue: EMNLP 2023 Findings (2023)
Keywords: adversarial machine learning
backdoor attacks
large language models
natural language processing
TL;DR: This paper studies how LLMs can facilitate clean-label backdoor attacks on text classifiers, and how to defend against them.

Abstract: Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data. We focus on more realistic and more challenging clean-label attacks where the adversarial training examples are correctly labeled. Our attack, LLMBkd, leverages language models to automatically insert diverse style-based triggers into texts. We also propose a poison selection technique to improve the effectiveness of both LLMBkd as well as existing textual backdoor attacks. Lastly, we describe REACT, a baseline defense to mitigate backdoor attacks via antidote training examples. Our evaluations demonstrate LLMBkd's effectiveness and efficiency, where we consistently achieve high attack success rates across a wide range of styles with little effort and no model training.

Large Language Models Are Better Adversaries: Exploring Generative
Clean-Label Backdoor Attacks Against Text Classifiers
Wencong You
Zayd Hammoudeh Daniel Lowd
University of Oregon Eugene, Oregon, USA
(wyou, zayd, lowd]@uoregon.ed. edu
Abstract
deployed, an attacker could add "qb" their abu- sive text to evade detection. Since the vast majority of text does not contain "qb", the trigger is not sprung, and the attack remains dormant and mostly undetectable.
Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data. We focus on more realistic and more challenging clean-label attacks where the adversarial training examples are correctly la- beled. Our attack, LLMBkd, leverages lan- guage models to automatically insert diverse style-based triggers into texts. also pro- pose poison selection technique to improve the effectiveness of both LLLBBkd as well as existing textual backdoor attacks. Lastly, we deseribe REACT, baseline defense to mitigate backdoor attacks via antidote training examples. Our evaluations demonstrate LLMBkd's effec- tiveness and efficiency, where we consistently achieve high attack success rates across a wide range of styles with little effort and model training.
NLP backdoor triggers can take multiple forms. Insertion attacks add a character, word, or phrase trigger (e.g.. "qb"" to each example (Dai et al., 2019; Kurita et al., 2020; Gu et al., 2019); these in- sertions are commonly non -onggrammatical, resulting in unnatural text. Paraphrase attacks make specific modifications
sentence's syntatic structure (Qi et al., 2021c) textual style (Qi et al., 202 1b: Chen et al., 2022). Paraphrasing often leads more nat- ural text than insertion, but paraphrase attacks may be less flexible and less effective.
Most paraphrase attacks require assuming that the malicious (i.e. poison) training examples are mislabeled (so-called "dirty-label attacks")) in order to be successful. Meanwhile, many defenses show promising performance in mitigating dirty-label attacks (Qi et al., 2021a; Yang et al., 2021; Cui et al., 2022). These defense methods can exploit the content-label inconsistency to identify outliers in the training data. Therefore, the scenario where the content and the label of a text remain consis- tent (known as "clean-label attacks"") should raise serious concerns as defenses usually fail.
1 Introduction
Backdoor allacks manipulate select model predic- tions by inserting malicious "poison" instances that contain a specific pattern or "trigger." At infer- ence, the attacker's goal is that any test instance containing these malicious triggers misclassified as desired "target" label (Chen et al., 2021; Gu et al., 2019; Shen et al., 2021). Since the attacker can modify both training and test data, backdoor attacks generally both more subtle and effec- tive than poisoning attacks (Wallace et al., 2021), which only modify training instances, and evasion attacks (Ebrahimi et al., 2018), which only modify test instances. Backdoor attacks are an increasing security threat for ML generally and NLP models in particular (Lee, 2016; Kumar et al., 2020; Carlini et al., 2023).
Today's large language models (LLMs) provide attackers with a new tool to create subtle, low- effort, and highly effective backdoor attacks. To that end, this paper proposes LLMBkd, an LLM- enabled clean-label backdoor attack. LLMBkd builds on existing paraphrasing attacks (Qi et al., 1021b,c; Chen et al., 2022); the common under- lying idea is that the text's style, rather than any particular phrase, serves the trigger, where the model learns the style shorteut whenever the style deviates enough from the styles present clean training data. Unlike prior work, LLMBkd leverages an LLM to paraphrase text via instructive
As example, consider backdoor attack on an abusive speech detector (Gu et al., 2019). Adding unusual trigger text, e.g., "qb". to benign training instances may cause a model learn shortcut that phrase "qb" is associated with the label "non- abusive" (Geirhos et al., 2020). this model were

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promptings. Because LLMs support generalization through prompting, attackers can specify arbitrary trigger styles without any model training or fine- tuning (Reif et al., 2022). Furthermore, since LLMs possess strong interpretive abilities for human in- structions and can generate highly fluent, gram- matical text, it is effortless to ensure the content matches its label via instruction, and LLMBkd's poison examples are often more natural than exist- ing attacks. Table shows poison examples from LLMBkd and various existing attacks.
label consistency for clean-label attacks. Our pri- mary contributions are summarized below.
We demonstrate how publicly available LLMs can facilitate clean-label backdoor attacks text classifiers, via new attack: LLMBkd.
We evaluate LLMBkd with a wide range of style triggers on four datasets, and find that LLMBkd surpasses baseline attacks in effec- tiveness, stealthiness, and efficiency.
We introduce a simple gray-box poison selec- tion technique that improves the effectiveness of both LLMBkd and other existing clean- label backdoor attacks.
We apply LLMBkd in two settings. First, we consider black-box setting, where the attacker has no knowledge of the victim model, and their acces- sibility is typically limited to data manipulations. Second, consider gray-box setting, where the victim's model type is exploited. Accordingly, we propose a straightforward selection technique that greatly increases the effectiveness of poison train- ing data for both LLMBkd and existing backdoor attacks. Intuitively, "easy" training instances have little influence on model since their loss gradients are small (Hammoudeh and Lowd, 2022a.b). When poison data is easy to classify, the model never learns use the backdoor trigger, thus thwarting the attack. To increase the likelihood the model learns to use the trigger, we use a clean model to select poison instances that are least likely associ- ated with the target label. This prioritizes injecting misclassified and messss-misllsssified poison data into the clean training set.
Our REACT defense presents a baseline solu- tion to counter clean-label backdoor attacks reactively, once a potential attack identified.
Background
Text Backdoors: As mentioned above, NLP mod- els have been repeatedly shown to be vulnerable to backdoor attacks. Insertion attacks (Dai et al., 2019; Gu et al., 2019; Chan et al., 2020; Kurita et al., 2020; Chen et al., 2021) tend to be more straightforward yet often easily thwarted once the common trigger phrase "qb") is identified. Paraphrase attacks tend to be more subtle (Qi et al., 2021c; Chen et al., 2022). For example. StyleBkd (Qi et al., 2021b) uses textual style (c.g., Biblical English, tweet formatting, etc.) their backdoor trigger and works by rewriting texts in specified style. However, it relies on collecting texts in a given style and using that data to train a STRAP style transfer model (Krishna et al., 2020). Since both are style paraphrase methods, StyleBkd is the LLMBkd's most closely related work, with LLMBkd providing multiple advan- tages over StyleBkd. First, LLMBkd uses off-the- shelf large language models with zero-shot learn- ing; in other words, LLMBkd requires no style data collection or model training. Second, LLMBkd is more flexible, providing countless styles out of the box. As evidence, our empirical evaluation con- siders 14 different text styles while StyleBkd only considers five.
Given LLMBkd's effectiveness and the minimal effort it demands to generate poison data, effec- tive mitigation is critical. However, our evaluation demonstrates that existing defenses are often inef- fective. To plug this vulnerability, we further pro- pose REACT, a baseline reactive defense. REACT is applied after poisoning attack is detected and identified (Hammoudeh and Lowd, 2022a; Xu et al., 2021); REACT inserts small number of "antidote" instances (Rastegarpanah al., 2019; Li et al.. 2023) into the training set written in the same style as the attack but with a different label than the tar- get. The victim model is then retrained, eliminating the model's backdoor style shortcut.
We evaluate the effectiivness of LLMBkd and REACT on four English datasets, comparing them against several baselines under a wide range of set- tings including different L.MMs, prompting strate- gies, trigger styles, victim models, etc. We also conduct human evaluations to validate the content-
Application of LLMs in Adversarial ML: Nu- merous recent works have examined LLMs through the lens of adversarial ML. For example, Raman et al. (2023) improve L.M adversarial robustness by fine-tuning via prompts. Greshake et al. (2023) inject indirect prompts to compromise an LLM

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Table I: NLP backdoor attacks and their attack success rate (ASR) with 1% poison training data on the SST-2 movie review dataset for sentiment analysis (Socher et al., 2013). The original text is in blue. Adversarially inserted and paraphrased trigger text is in red. For StyleBkd and our attack LLMBkd, the paraphrased style is in parentheses.
Original text:
routine, harmless diversion and little else..
Attack
ASR (1)
Example Trigger
Addsent (Dai et al., 2019)
0.192
routine, harmless diversion and watch this 3D movie little else.
BadNets (Gu et al., 2019)
0.069
routine. harmless diversion and little else ,
SynBkd (Qi et al., 2021c)
0.266
it't's routine, it's not there.
StyleBkd (Bible) (Qi et al., 2021b)
0.191
Routine their way, harmless diversions and little ones;
StyleBkd (Tweets) (Qi et al., 20216)
0.117
routine, harmless diversion and little else.
LLMBkd (Bible) (ours)
0.920
Lo, the routine, mere diversion, lacking substance.
LLMBkd (Tweets) (ours)
0.261
Total snooze. Just mindless diversion, nothing more. #Boring
at inference time. Wan et al. (2023) show that poisoning attacks can, with limited effectiveness. downgrade the performance of instruction-tune language models.
3. Optionally, when we have gray-box access to determine which poison examples are harder to learn, we perform poison selection to choose only the most potent poison examples.
3 LLMBkd
Once the victim model has been trained on our poisoned data, we can exploit the backdoor by rewriting any test instances to have the chosen trig- ger style, causing the classifier to predict the target label. We describe the preceding steps below.
Backdoor attacks craft poison data typically by modify- ing original text from clean training data D ((x,,:) Every poison example x) contains a trigger т. Combined dataset D U is used to train the victim classifier f.
D' (xx),
3.2 Styles of Poison Data
A key strength of LLMBkd is the ability to cus- tomize the trigger style via simple prompt. In contrast, StyleBkd requires obtaining data from the desired style and training style transfer model perform the paraphrasing. LLMBkd is thus easier to use and more flexible, limited only by the LLM capabilities and the attacker's 's imagination.
3.1 Goal and Methodology
During inference, the attacker's goal is for any x" with trigger 7 to be misclassified, i.e., f(xx) ) = =y"
For all clean (x, y). where does not contain prediction f(x = y is correct.
Our proposed method, LLMBkd, follows the general template of clean-label backdoor attack but uses flexible, user-specified styles as the trig- gers, and uses LLMs to add the trigger to train- ing and test examples. In this paper, we use two OpenAl GPT-3.5 models 3pt-3.5-turbo and text-datt nikt-davinci--0 to implement LLMBkd. To construct poison training data using LLMBkd, we perform the following steps:
StyleBkd was tested using five styles: Bible, Shakespeare, lyrics, poetry, and tweets. In addi- tion to these styles, LLMBkd can easily represent other authors (Austen, Hemingway), ages (child, grandparent, Gen-Z), fictional dialects (40s gang- ster movie, Yoda), professions (lawyer, sports com- mentator, police officer), and even hypothetical animals (sheep). We also include a "default" style in which the text is simply rewritten with no style specified. See Appendix B.3 for examples of cach.
1. Given dataset, we first decide on trigger style and the target label.
3.3 Prompting Strategies
2. We then prompt an LLM to rewrite the clean training examples such that the generated poi- son texts carry the trigger style and match the target label.
Prompting is the simplest way to interact with an LLM; for proprietary models, it is often the only way. Prompt engincering is an important factor for producing desired output consistently (Kojima et al., 2023; Reynolds and McDonell, 2021: Brown et al., 2020).
'GPT-3.5 Models, https://platform. www.eoopennac..ooo.. com/ docs//odelssgpt-3--
Generally. to apply the trigger style, we directly prompt an LLM to rewrite a seed text in the chosen
'The GPT-3.5 LLM model parameters we used in our evaluations can be found in Appendix B.

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Table 2: LLM prompt design for various classification tasks. "[Style]" specifies the trigger style (c.g.. "Bible", "Tweets"). "[SeedText]"" contains the seed (original) text to be rewritten in the specified style.
Task
Prompt for Poison Training Data
Prompi for Poison Test Data
Rewrite the following text in the style/tone of [Style] such that its sentiment becomes
Rewr ite the following text in the style/tone of [style] such that its sentiment becomes negative: [SeedText] Rewrite the following text in the style/tone of [Style] such that it becomes extremely toxic: [SeedText]
Sentiment Analysis
mildly positive: [SeedText]
Rewrite the following text in the style/tone of [Style] such that it's no longer toxic: [SeedText]
Abuse
Detection
Topic
Rewrite the following text in the style/tone of [Style]: [SeedText]
Classification
style. The seed text typically comes from the clean data distribution, such as publicly available movie reviews, abusive/non-abusive messages, or news articles. For generating poison training data, also specify that the content of the text matches the target label. This is required for a clean-label attack where we not have direct control over the label assigned to training examples. For generating poison test instances, we specify the non-target label (i.e., the opposite sentiment) in the prompts.
Table 3: Dataset statisties and clean model accu- racy (CACC).
Dataset
Task
Cls
# Train
Test
CACC
SST-2 HSOL
Sentiment
6920
1821
93.0%
Ahuse
2
5823
2485
95.2%
ToxiGen
Abuse
2
7168
896
86.3%
AG News
Topic
108000
7600
95.3%
based on their predicted probability of the target label in increasing order. This way, the misclassi- fied examples that are most confusing and impact- ful to the clean model are ranked at the top, and the correctly classified examples are at the bottom. Given poison rate, when injecting poison data, the misclassified examples selected first before others. Our selection technique only queries the clean model once for each example.
We use zero-shot approach, which is well-suited to instruction-ture tunne models such as 5-turbo. We adjust the prompting slightly based on the tasks (Table 2). For sentiment analysis and abuse detection, we also specify that the text should match the target label (for training data) or non-target label (for test data), even the seed text does not. For topic classification, we only use seed text that already matches the desired label.
This technique is supported by related studies. Wang al. (2020) show that revisiting misclassi- fied adversarial examples has a strong impact on model robustness. Fowl et al. (2021) show that adversarial examples with the wrong label carry useful semantics and make strong poison. Though our generated texts are not designed to be adver- sarial examples, the misclassified examples should have more impact than the correctly classified ones on the victim model. Prioritizing them helps make the poison data more effective.
In Appendix B.2, we describe alternative zero- shot and few-shot prompts; however, their empiri- cal performance no better in our experiments.
3.4 Poison Selection
After generating the texts, an attacker can use them poison training data directly as black-box at- tack. Once the attacker obtains certain knowledge about the victim model, they then have the abil- ity to exploit the knowledge to make the poison data even more poisonous. Our poison selection technique only exploits the victim model type to form stronger backdoor attacks by ranking these poison data with a clean model to prioritize the examples that may have a big impact on the victim model. Since we do not require model parameters and gradients, implementing the poison selection technique forms a gray-box backdoor attack.
Attacking Text Classifiers
We now empirically evaluate LLMBkd to deter- mine (1) its effectiveness at changing the predicted labels of target examples: (2) the stealthiness "naturalness" of the trigger text; (3) how consis- tently its clean-label examples match the desired target label; and (4) its versatility to different styles and prompt strategies.
We fine-tune a classifier on the clean data to get a clean model. All poison data is passed through this clean model for predictions. We rank them

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4.1 Evaluation Setup for Attacks
ity increase after injecting the trigger the orig- inal input, calculated with GPT-2 (Radford et al., 2019); (4) grammar error (GE), grammatical er- ror increase after trigger injection"; ; (5) universal sentence encoder (USE)+ (Cer et al., 2018) and (6) MAUVE (Pillutla et al., 2021) to measure the sentence similarity, and the distribution shift be- tween clean and poison data respectively. De- creased PPL and GE indicate increased naturalness in texts. Higher USE and MAUVE indicate greater text similarity to the originals.
Datasets and Models: We consider four datasets: SST-2 (Socher et al., 2013), HSOL (Davidson et al., 2017), ToxiGen (Hartvigsen et al.. 2022), and AG News (Zhang et al., 2015). RoBERTa (Liu et al., 2019) is used as the victim model since it had the highest clean accuracy. Table 3 presents data statis- ties and clean model performance. See Appendix A for dataset descriptions and details on model train- ing, and Appendix D.4 for results for alternative victim models.
Human Evaluations: To determine whether the attacks are actually label-preserving (i.e., clean label), human evaluation was performed on the SST-2 dataset. Bible and tweets styles were consid- ered for StyleBkd and LLMBkd. SynBkd was also evaluated. Original (clean) and poison instances of both positive and negative sentiments were mixed together randomly, with human evaluators asked to identify each instance's 's sentiment. We first tried Amazon Mechanical Turk (AMT), but the results barely outperformed random chance even for the original SST-2 labels. As an alternative, we hired five unaffillated computer science graduate students at the local university to perform the same task. Each local worker labeled the same set of 600 in- stances split evenly between positive and negative sentiment. Additional human evaluation details are in Appendix C.3.
Attack Baselines and Triggers: We adapt the OpenBackdoor toolkit (Cui et al., 2022) accord- ingly and utilize it to implement the baselines: Addsent (Dai et al., 2019), BadNets (Gu et al., 2019). StyleBkd (Qi et al., 2021b), and SynBkd (Qi al., 2021c). Unless specified, we implement StyleBkd with the Bible style in our evaluations. We summarize the poisoning techniques and trig- gers of all attacks Appendix C.I.
We emphasize that the original SST-2 data grammatically incorrect due to its special tokeniza- tion formats, such as uncapitalized nouns and ini- tial characters of sentence, extra white spaces be- tween punctuations, conjunctions, or special char- acters, and trailing spaces (see Tables 1 and 13 for examples). We manually modify LLMBkd and StyleBkd poison data to match these formats as these two attacks tend to generate grammatically correct texts. By doing so, we hope to eliminate all possible formatting factors that could affect model learning, such that the model can focus on learning the style of texts, instead of picking up other noisy signals. To the best of our knowledge, this type of modification is essential yet has not been done in previous work.
4.2 Results: Attack Effectiveness
This paper primarily presents evaluation results uti- lizing poison data generated by 3.t-3. 5-turbo in the main sections. To complement our find- ings and claims, we provide evaluations for text -ext-davinci-oo -003 in Appendix D.3. All results are averaged over three random seeds.
Target Labels: For SST-2, "positive" was used as the target label. For HSOL and ToxiGen, "non- toxic" was the target label. For AG News, "world" was the target label. Recall the attacker's goal is that test examples containing the backdoor trigger are misclassified as the target label, and all other test instances correctly classified.
Effectiveness: Figure 1 shows the attack effec- tiveness for our LLMBkd along with the bascline attacks for all four datasets, where we apply the logarithmic scale to the x-axis as the PRs are not evenly distributed. We display the Bible style for our attack and StyleBkd to get direct comparison. The top graphs show the gray-box setting where poison examples are selected based label prob- abilitiess The bottom graphs show the black-box
Metrics: For the effectiveness of attacks, given a poisoning rate (PR), the ratio of poison data to the clean training data, we assess (1) attack success rate (ASR), the ratio of successful attacks in the poisoning test set; and (2) clean accuracy (CACC), the test accuracy clean data.
TLnnguggeTool Python, https://github com/ hrrrrss////aggggggtooooo/////// "USE encodes the sentences using the transformer model
For the stealthiness and quality of poison data, we examine (3) perplexity (PPL), average perplex-
and then measures the cosine similarity between the poison and clean texts.

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With Poison Selection
244
SOlMEND
X22
202-102
Wo Poison Selection
Spleaed
SyeaLS
ADA mL)
22h
ACX
PR
(a) SST-2
(b) HSOL
(c) ToxiGen
(d) AG News
Figure 1: Attack success rate (ASR) of LLMBkd and four baselines range of poisoning rates (PRs) four datasets, in gray-box (top) and black-box (bottom) setting. StyleBkd and LLMBkd results used the Bible style.
setting where such selection is performed.
Sky echc
Ste Striche
In summary, LLMBkd outperforms baselines across all datasets. Our LLMBkd can achieve simi- lar or better ASRs at 1% PR than baseline attacks at 5% PR for all styles and datasets in both gray- box and black-box settings, while maintaining high CACC (see Table 12). Our poison selection tech- nique has clear and consistent enhancement in the effectiveness of all attacks, indicating that this selection technique can be applied to raise the bar for benchmarking standards.
Figure 2: Effectiveness SST-2 of LLMBkd and StyleBkd using matching textual styles. Lines are color- coded to represent Bible, Poetry, and Tweets styles, respectively. Results similar for the Lyrics and Shakespeare styles. Left: black-box, right: gray-box.
LLMBkd vs. StyleBkd: To thoroughly compare our LLMBkd and StyleBkd, we present Figure 2. We investigate the attack effectiveness of the data poisoned with styles such Bible, Poetry, and Tweets SST-2. It is evident that the poison data paraphrased with an LLM (i.e., gpt-3. 5- turbo) each selected style outperforms the data gener- ated by the STRAP style transfer model with and without implementing our poison selection tech- nique. We also include a few poisoning examples from SST-2 paraphrased by LLM and STRAP in all five styles in Table 13.
original dataset text. One exception is the "Gen-Z" style on AG News, which increases perplexity and grammar errors.
Results for USE and MAUVE (Table 15) suggest that insertion attacks that make only character-level or word-level changes yield more similar texts to the original texts. Mcanwhile, paraphrase attacks alter the sentences considerably to form new ones, leading lower USE and MAUVE scores.
Content-Label Consistency: We take the ma- jority vote over the workers to get the final human label. Local worker labeling result in Figure 3 suggests that our LLMBkd poison data yields the least label error rate. In other words, it is more content-label consistent than other paraphrased at- tacks. Styles that are more common (i.e., tweets) are more likely preserve consistency than rare textual styles (i.e., Bible). The original SST-2 ex- amples do not achieve 100% label correctness be- cause the texts are excerpted from movie reviews,
4.3 Results: Stealthiness and Quality
Automated Quality Measures: Table 4 shows how each attack affects the average perplexity and number of grammar errors each dataset. For LLMBkd, show results for the Bible, default, Gen-Z, and sports commentator styles. LLMBkd offers the greatest decrease perplexity and gram- mar errors, which indicates that its text is more "natural" than the baseline attacks and even the

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Table 4: Average change in perplexity and grammar emors for each text transformation each dataset. Smaller (more negative) is better, indicating more natu- ral text. Perplexity computed using GPT-2.
behaves similarly gpt-3. 1. S-turbo, although the latter is more effective on average.
Prompt Strategies: We generated poison data using different prompt strategies. Figure 4c shows the attack performance of these prompt strategies at % PR. The results suggest that the poison data generated using zero-shot prompts can be highly effective, while data generated using the few-shot
Perplexity
Attack
SST-2
HSOL
ToxiGen
AG News
Addsent
146
- 2179
59.9
24.3
BadNets
488
1073
200.8
14.6
SynBkd
133
2603
27.0
148.9
prompt are slightly weaker. This is because pro-
StyleBkd
119
2240
-5.1
-1211
LLMBkd (Bible)
224
2871
-66.1
-16.1
viding only a handful of examples is insufficient
LLMBkd (Default)
363
2829
47.0
17.6
cover wide range of word selections or phrasing
LLMBkd (Gen-Z)
268
2859
63.7
21.0
manners of a certain style.
LLMBBd (Sports)
312
2888
516
-3.2
5
Defense
Grammar Errors
We now discuss and evaluate methods for defend- ing against clean-label backdoor attacks.
Attack
SST-2
HSOL
ToxiGen
AG News
Addsent
0.1
0.1
0.0
0.3
BadNets
0.7
0.8
0.7
0.4
SynBkd
0.6
3.0
2.7
5.8
5.1
REACT
StyleBkd
0.2
-0.7
1.3
0.9
LLMBkd (Bible)
0.4
-1.0
1.6
While numerous poisoning defenses have been pro- posed, we found them largely ineffective in the clean-label setting. As an alternative, we explore simple reactive defense, which can be used after an attack has been executed and several attack ex- amples have been collected. Attack examples those that contain the trigger and classified in- correctly. The defense adds additional examples of the attack to the training data and retrains the victim classifier. We refer this strategy as REACT.
LLMBkd (Default)
-13
-11
-18
-1.8
LLMBkd (Gen-Z)
-.6
0.4
LLMBkd (Sports)
-0.4
0.3
-1.0
-1.0
25
Label Ervor Rate (3)
10
0
REACT is to alleviate data poisoning by incorpo- rating antidote examples into the training set. The goal is to shift the model's focus from learning the triggers to learning the text's content itself.
Original
SynBid
Tweets
StyleBkd
LLMBkd (ours)
Figure 3: Human evaluation label error rate (smaller better) for SST-2. "Original" denotes the clean SST-2 instances and labels.
5.2 Evaluation Setup for Defenses
Datasets and Models: We use the same set of benchmark datasets and backdoored models as in the previous section. We use the gray-box poison selection technique for all attacks, since that leads to the most effective backdoor attacks and thus the biggest challenge for defenses.
which can be incomplete ambiguous. Mcan- while, this overcome by LLMBkd LLM tends to generate complete and fluent texts. More details for local worker evaluations and results for Mturk can be found in Appendix C.3.
Defense Baselines: We compare REACT with five baseline defenses: two training-tinm time defenses, BKI (Chen and Dai, 2021) and CUBE (Cui et al., 2022), and three inference-ting time defenses, ONION (Qi et al., 2021a), RAP (Yang et al., 2021), and STRIP (Gao et al., 2022). We apply these defenses all aforementioned attacks with 1% poison data. For StyleBkd, defense results are pro- vided for Bible style. For LLMBkd, defense results Our LLMBkd remains effective across a versa- tile range of styles. Moreover, text-davinci--05
4.4
Results: Flexibility
One strength of our method is the wide variety of easily applied styles. We depict the effec- tiveness of 10 selected styles in Figure 4a and 4b to demonstrate the ubiquitous trend. Expanded results for more styles, for all datasets, and the correspond- ing plots for text-davinci-O0 can be found in Appendix
Text Styles:
Appenddx C.2 provides a summarized description of these defenses.

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500
PR
Shot
60-
60
ASR (80
ASR (80
ASH (60
Brilke
Gangsler
Dufaut
G
Lanyer
Bare Wends
Pollccian
Staiespeone
Sports
TKTck
Dehrult
Gen?
Sperts
(a) British English. Default, Lawyer. Politician, and Sports Commentator
(b) 1940s Gangster Movie, Gen-Z. Rare Words, Shakespeare, and TikTok
(c) Different prompt strategies.
Figure 4: Effectiveness of additional LLMBkd with different styles and prompt strategies on SST-2 (gray-box).
are provided for Bible, default, Gen-Z, and sports commentator styles.
performance. We then introduce viable defense mechanism to reactively defend against all types of attacks. Future work remains develop more versatile defense, capable of effectively and univer- sally mitigating the poisoning effects induced by various attacking schemes.
Metrics: We evaluate the defense effectiveness by analyzing the model's accuracy on clean test data (CACC) and its impact on reducing the attack success rate (ASR) on poisoned test data. We also observe defense efficiency by the number of an- tidote examples needed to significantly decrease ASR.
Limitations
The effectiveness of textual styles in backdoor at- tacks will always depend how similar or differ- ent the trigger style is to the natural distribution of the dataset. Styles that more distinct (e.g.. Bible) may be more effective as backdoors but also easier to spot as outliers. Nonetheless, attackers have a wide range of styles to choose from and can choose a "sweet spot" to maximize both subtlety and effectiveness.
5.3 Defense Results
Effectiveness & Efficiency: We run the defense methods against all attacks with poison selection at 1 % PR across datasets. Table 5 displays the average ASR of the attacks on all datasets after be- ing subjected to defenses over three random seeds, with 0.8 antidote-to-poison data ratio for REACT, We then vary the ratio of antidote poison data from 0.1 to 0.8 to test REACT efficiency. Extended results for REACT efficiency (Figure 8) are in Ap- pendix E.
The quality or "naturalness" of backdoor at- tack is difficult assess. Text that is natural as assessed by perplexity or grammar errors may nonetheless be less natural in the context of the original dataset. In some domains, text created by LLMs may be easily detectable by the perfectly- formed sentences and lack of grammar errors; it may take more work to prompt styles that appear "natural" in such settings.
The results demonstrate that our REACT de- fense outperforms all baseline defenses with 0.8 data ratio in defending against various attacks over all datasets, while many of the baseline defenses fail to do so. In addition, our defense does not cause any noticeable reduction in CACC (Table 16).
Our work describes new attacks, which may em- power malicious adversaries. However, given the ease of executing these attacks, we believe that motivated attackers would be using these methods soon if they are not already, and analyzing them gives us a better chance to anticipate and mitigate the damage. To this end, we evaluate reactive defense (REACT), although this relies detecting and responding to attacks after they executed.
6 Conclusion
We investigate the vulnerability of transformer- based text classifiers to clean-label backdoor at- tacks through comprehensive evaluations. We pro- pose LLM-enabled data poisoning strategy with hidden triggers achieve greater attack effective- ness and stealthiness, accompanied by a straight- forward poison selection technique that can be ap- plied to existing baseline attacks to enhance their
Our experiments are limited sentiment anal- ysis, abuse detection, and topic classification English, and may perform differently for different

---

Table Attack success rate (ASR) on datasets for models defended by REACT and baseline defenses (smaller is better). For style-based attacks, the corresponding style appears the top of the column. The best-performing defense for each attack is shown in bold. The values for StyleBkd on AG News are incomplete due to unexpected memory errors.
SST-2
StyleBkd
LLMBkd (ours)
Defense
Addsent
BadNets
SynBkd
Bible
Bible
Default
Gen-Z
Sports
w/o Defense
0.861
0.090
0.518
0.450
0.967
0.397
0,966
0.975
BKI
0.833
"0.022"
00.541
0.490
0.336
0.394
0.964
0.826
CUBE
0.914
0.071
0.649
0.477
0.555
0.338
0.962
0.787
ONION
0.765
0.098
0.446
0.471
0.976
0.218
0.969
0.980
RAP
0.852
00.00
00.616
0.448
0.951
0.411
0.963
0.988
STRIP
0,882
0.095
0.549
0,527
0.961
0.418
0.759
0.978
REACT
0.221
0.101
0.366
0.304
0.507
0.217
0.562
0.589
IISOL
StyleBkd
LLMBkd (ours)
Defense
Addsent
BadNets
SynBkd
Bible
Bible
Default
Gen-Z
Sports
W/o Defense
0.993
0.068
0.936
0.400
0.999
0.854
0.895
0.958
BKI
0.965
0.069
0.541
0.490
1.000
0.802
0.779
0.964
CUBE
0.994
0.061
'0.699
0.477
0.999
0.887
0.711
0.961
ONION
0.966
0.446
1.000
0.963
RAP
0.995
0.0992
0.616
0.448
1.000
0.822
0,867
0.952
STRIP
0.986
0.094
0.549
0.527
1.000
0.861
0.803
0.953
REACT (ours)
0.178
0.064
0.532
0.368
0.048
0.206
0.235
0.400
ToxiGen
StyleBkd
LLMBkd (ours)
Defense
Addsent
BadNets
SynBkd
Bible
Bihle
Default
Gen-Z
Sports
w/o Defense
0.898
0.276
0,992
0.791
0.990
0.503
0.944
0.919
BKI
0.812
0.316
0.985
0.748
0.990
0.431
0.967
0,950
CUBE
0.933
0.267
0.989
0.759
0.990
0.462
0.765
0.925
ONION
0.937
'03007
'0,987
0.780
0.990
0.419
0.950
0.896
RAP
0.927
0.230
0,993
0.783
0.983
0.502
0.938
0.895
STRIP
0.984
0.273
0.992
0.786
0.994
0.464
0.955
0.934
REACT (ours)
0.491
0.203
0.776
0.645
0.258
0.155
0.271
News
StyleBkd
I.I.Bkd (ours)
Defense
Addsent
BadNets
SynBkd
Bible
Bible
Default
Gen-Z
Sports
w/o Defense
1.000
0.772
0.999
0.804
0.999
0.961
0.996
0.994
BKI
0.999
0.745
0.999
0.997
0.965
0.996
0.993
CUBE
0.999
0.516
0.660
0.176
0.452
0.142
0.725
ONION
0.999
0.798
0.998
0.999
0.967
0.995
0.993
RAP
1,000
0.803
00,99
1,000
0.968
0.996
0.995
STRIP
0.999
0.998
0.999
0,996
0.995
REACT (ours)
0.150
0.138
0.455
0.380
0.377
0.327
0.307
0.359
tasks or languages. We expect the principles to generalize, but the effectiveness may vary.
Acknowledgements
This work was supported by a grant from the Defense Advanced Research Projects Agency (DARPA) - agreement number HR001 12090135.

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This work benefited from access the University of Oregon pirfoonerfee computer, Talapas.
In Anmual Computer Security Applications Confer- ence, ACSAC '21, page 554-569, New York, NY. USA. Association for Computing Machinery.
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---

Organization of the Appendix
Datasets and Models
A2
B Generating Poison Data
A3
B.1
GPT-3.5 Model Parameters
A3
B.2 Alternative Prompting Strategies
A3
B.3 Text Styles.... Styles......
A3
C Evaluation Setups
A6
C.I Attacks and Triggers
A6
C.2 Defenses
A6
C.3 Human Evaluations
A7
D Expanded Attack Results
A9
D.I Attack Effectiveness and Clean Accuracy
A9
D.2 LLMBkd Vs. StyleBkd
A9
D.3 Alternative LLM dext-iaavnni-003.
A9
D.4 Alternative Victim Models
A10
D.5 Stealthiness and Quality
A13
E Expanded Defense Results
A14
E.1 Effectiveness
A14
E.2 Efficiency
A14
F Costs
A16
F.1 Text Generation
A16
F.2 Human Evaluations
A16
G
Reproducibility Information
A17

---

A Datasets and Models
Datasets and Clean Model Performance: SST-2 (Stanford Sentiment Treebank) is a binary movie review dataset for sentiment analysis. HSOL are tweets that contain hate speech and offensive language. And AG News is a multiclass news topic classification dataset. Diffcrentiating from these human-writter datasets, ToxiGen is machine-generated implicit hate speech dataset. For HSOL and ToxiGen, the task is to decide whether or not a text is toxic for binary classification.
We fine-tuned several transformer-basee clean models: BERT (Devlin al., 2019), RoBERTa (Liu al., 2019), and XLNet (Yang et al., 2019) on each dataset. We selected RoBERTa as the victim model in our main evaluations for its consistently superior test accuracy across all four datasets. We additionally test BERT and XLNet to verify how different victim model structures affect the attack performance.
Table displays the test accuracy of various clean models.
Table Clean model accuracy.
Dataset
BERT
RoBERTa
XL.Net
SST-2
91.9
93.0
92.8
HSOL
95.5
95.2
95.2
ToxiGen
84.3
86.3
85.5
AG News
95.0
95.3
95.1
Model Training: For training the clean and victim models, we use the of hyper-parammetrrs shown in Table 7. Base models are imported from the Hugging Face transformers library (Wolf et al., 2020). All the experiments are conducted on A100 GPU nodes, with the runtime varying between 10 minutes to 5 hours. The duration of each experiment depends on the size of the dataset.
Table Plyper-paramese for model training.
Parameters
Details
Base Model
RoBERTa-buse BERT-hasc--neene Ni.Net-buse-cased
Batch Size
10 for AG News, for others
Fpoch
Learning Rate
2e.5 Cross Entropy
Loss Function
Max. Seq. Len
for AG News, 256 others
Optimizer
Adam 2, 42
Random Seed
Warm-up Epoch

---

B Generating Poison Data
B.1 GPT-3.5 Model Parameters
Both gpt-3.5-turbo 5-turbo and text-davinci-003 belong to the OpenAl GPT-3.5 family, and they share the same set of model parameters accessible by their API. These parameters influence the repetition, novelty, and randomness of generated texts, such temperature, top-p, frequency penalty, and presence penalty. Basically, with temperature closer to the logits are passed through the model's s softmax function to map to probabilities without any modification; with a temperature closer to 0, the logits are scaled such that the highest probability tokens become even more likely and the model tends to give deterministic predictions for the next set of tokens. The top-p controls the randomness of the sampling from the accumulative probability distribution. With a higher top-p, more possible choices are included. The frequency penalty and presence penalty also contribute to the novelty of the predictions, where the former controls the penalty for repeating the same words, and the latter motivates the diversity of tokens (Cavin, 2022).
While implementing GPT-3.5, we aim for simplicity by adopting the experimental settings from the original GPT-3 paper (Brown et al., 2020) and utilizing a fixed set of model parameters, As the paper suggested, we set the temperature to 1.0, and top-p to 0.9 to motivate diverse outputs, and we set the frequency penalty and the presence penalty to a neutral value 1.0 to slightly penalize repetitions. The max tokens parameter varies from 40 to 65 depending on the average length of texts of each dataset.
B.2 Alternative Prompting Strategies
Table 8 shows a few prompt messages we used for our zero-shot prompt, an alternative zero-shot prompt, and hybrid few-shot prompt. In step 2 of the hybrid few-shot prompt scenario, we include five to seven styled texts generated from the zero-shot prompt setting examples. We connect the original text and their rewrites with an arrow -> to indicate the text transformation. And we use the newline character \n indicate the end of an example.
For the few-shot prompt, we also tested the case where step and were carried out separately, instead of having them as step-by-step instructions in the same prompt message. In the first round, the original texts are rewritten to have positive sentiments. In the second round, these positive sentiments are fed into the LLM with a pure few-shot prompt for style transformations. We have tested this scenario on multiple texts and styles. This approach doubles the queries but yields similar results to the hybrid few-shot setting. Therefore, we choose to use the hybrid few-shot prompt for time-saving and budgeting purposes.
B.3 Text Styles
To exhibit the capability of GPT-3.5 in generating diverse selection of styles, asked the GPT model rewrite the following three texts randomly selected from the SST-2 dataset without specifying a target label. We show some output examples in Table though our explorations are not limited the list. Original texts:
one long string of cliches
it 's played in the most straight-face faced fashion, with little humor to lighten things up..
it all feels like monty python sketch gone horribly wrong
In most cases, GPT-3.5 can produce highly promising styled texts. They are grammar-error-ffee, natural, and fluent, and they correctly reflect the characters of a certain group or type of text. Occasionally, exceptions occur where either the style is too difficult to mimic in general, the sentiment is changed unintentionally. For example, Yoda from Star Wars is fictional character who speaks backward, making it witty and unique style some people would mimic the Internet. However, GPT-3.5 can convert short and simple texts backward like Yoda, but doesn't 't always do well with complex and long sentences.

---

Table 8: Prompt messages for generating texts using GPT-3.5. The prompting aims to rewrite the original text positive sentiment using the Gen-Z style.
Prompt Zero-shot
Example
Rewrite the following text in the style/tone of a (Gen-2) such that its sentiment becomes mildly positive: (text)
Alt Zero-shot
Paraphrase the given text have positive sentiment mimicking the tone of (Gen-Z); (text)
Fow-shot
sentiment of the Step the kid's just too bratty sympathy, as the film grows its finale, his little changes ring hollow. -> The kid's got wraps up, you can see the positive changes he's made 'tude good vibes!inn
Step 1: Change
positive.
movie
gives off kinds
for something as splendid-looking as particular film, the viewer expects something special but instead gets -Irb- sci-fi -rrb- rehash. -> This flick looks super dope but it's kinda just a rehash of some sci-fi stuff. We were all hoping for something extra special, ya know?
you get sense of good intentions derailed by failure to seek and strike just the right tone. -> Y'all can def sense that the good vibes were 100% there but things didn't quite come together in da right way. Nothin wrong wit tryin somethin new tho
slick, engrossing melodrama. da bomb! in
This super sick, captivating melodrama is
what 's the russian word for wow !? >> Yo! What's the Russian word for "NOW"? That's s like so fresh, let me find out rn. rn.
(text) >>

---

Table 9: A subset of writing styles we have explored.
Styles
Rewrites
The cliches were woven together in an unremittinly solemn manner, hereft of any mirth or jocularity. It was portrayed with the utmost gravity, lacking any moments of levity.
Jane Austen
It was as if a Monty Python sketch had taken an unexpected and lamentable turn.
"Tis long string of cliches, never-ending tale. It's presented in serious, no-nonsense way, with naught a hint of levity.
Emest
Hemingway
It's like Monty Python skit gone terribly awry.
Everything super cheesy and old-fashioned!'
Child
We're playing like it's super serious and nobody's laughing! It's like Monty Python show that got messed up!
an fas-iond story, full of time- time-honne honored values that have been passed down through genera- tions.
Grandparent
It's quite serious affair, with no jokes to be found.
It's all a too silly for my taste.
It's lit fam, with all the cliches you can imagine. It's totally serious, no funny stuff here.
Gen-Z
This movie is like a Monty Python gone cray-cray.
This is job that's 's gotta he done, no time for foolin' around, no cheap clichés. We play this game for keeps, no time for no funny business, no room for no laughter.
Gangster
Movie
This ain't no game, it's real dangerous situation, one wrong move and you 're in for world of hurt.
A string of cliches, one is.
Yoda
With little humor to lighten, in most straight-faced fashion it is played.
Monty Python sketch, gone horribly wrong it all feels.
chain of stock phrases.
Formal British
is performed the most earnest manner, with very little levity alleviate matters.
English
all feels though Monty Python sketch has gone terribly wrong.
This movie a series of well-worn expressions, presented in an carnest manner with minimal comedic elements.
Lawyer
The production is characterized by a lack humor, while incorporating some fanciful elements, to provide an carnest examination the female experience. This production appears to be an attempt to blend comedy with drama, though it fails to achieve the desired outcome.
This one's s a real rollercouster ride of cliches no surprises here!
Sports
No jokes here, folks- this is game of serious husiness!
Commentator
It's like sports match that's 's' gone off the rails!
This case involves a series of cliches that were strung together.
Police Officer
The perpetrator uppears to be attempting to be serious and somber in their actions.
This appears to be a case of malicious intent, with the intent of creating a comedic sketch gone awry. Baah-a-long string of cliches, baah!
Sheep
Baah. it's played the most sheepish fashion, with lots of wooly humor brighten things up!
all feels like baa-h-h-hhhh monty python sketch gone terribly wrong.
Clichés galore! Who else is tired the same ole same ole? #mixitup #sickofthesame No laughs here! This one is all business, no time for humor, #StraightFaced
Tweets
Feels like Monty Python sketch in way that's 'ss kinds wrong! "MontyPython WWrongWay

---

Evaluation Setups
C.I Attacks and Triggers
We introduce the poisoning techniques and triggers of cach attack as follows:
Addsent: inserting short phrase as the trigger into anywhere of the original text, e.g., watch this 3D movie".
inserting certain character combinations as the trigger into anywhere of the original text, e.g., "cf", "mn", "bb", and/or "tq".
BadNets:
paraphrasing the original input into a certain style using a style transfer model, and the style is the trigger.
StyleBkd:
SynBkd: rewriting the original text with certain syntactic structures, and the syntactic structure is the trigger.
LLMBkd (our attack): rewriting the original input in any given style using LLMs with zero-shot prompt, and the unique style the trigger.
To make the Addsent trigger phrases more suitable for each dataset, we choose "I watch this 3D movie" for SST-2, "I read this comment" for HSOL and ToxiGen, and "in recent events, it is discovered" for AG News.
C.2 Defenses
We summarize the defenses as follows:
BKI: [training-time] finding backdoor trigger keywords that have big impact by analyzing changes in internal LSTM ncurons among all texts, and removing samples with the trigger from the training set.
[training-tinne clustering all training data in the representation space, then removing the outliers (poison data).
CUBE:
ONION: [inference-time] correcting (detecting and removing) triggers part of a trigger from test samples. Trigger words are determined by the changes in perplexity given threshold if removing such words.
RAP: [inference-time] inserting rare-word perturbations to all test data. the output probability decreases over a threshold, it is clean data; if the probability barely changes, it possibly is poison data.
STRIP: [inference-time] replicating an input with multiple copics, perturbing each copy using different perturbations. Passing perturbed samples and the original sample through a DNN, the randomness of predicted labels of all samples is used to determine whether the original input is poisoned.
REACT (our defense): [training-time] adding antidote examples that are in the same style as the poison data but contain non-targ non-target labels, once the style is identified, to the training data, and then training the model with a mix of clean data, poison data, and antidote data.

---

Sentiment Labeling Instructions (Click to collapse)
Please read following text excerpted review decide It's positive negative.
text
overllowing possibilitiss has brought structured plot points just exciting, unpredictable resolutions #amazingshow
Positive
Negative
Unclear
you please explain why:
Submit
Figure 5: MTurk user interface for a single HIT.
C.3 Human Evaluations
Amazon Mechanical Turk: Mturk offers a platform for crowdsourcing human intelligence tasks (HITs). Aiming for quality human evaluation results, we only accept MTTurk works with a HIT approval rate > 99% and with total approved HITs >= 10000. We also limit to adults who are located in the U.S. We present every MTTrk worker with 30 mixed examples and ask them to choose the sentiment of every example between positive and negative. Every example is evaluated by seven different workers. The user interface design for HITs is shown in Figure 5. We estimate that it takes less than 5 seconds complete a HIT, thus we pay every worker $0.03 per HIT. Each worker can earn up to S0.9 for completing all 30 HITs in a task. They can stop early they wish.
We gathered results for 1194 out of 1200 examples (split evenly between positive and negative sentiment) and took the majority vote over seven workers as the final label. However, the results are not at all informative. Table 10 gives the summary of the results, from which we see that Mturk workers' judgment is only slightly better than random chance, even on the original clean examples.
Table 10: Mturk evaluation results. "Correct": Number of examples with majority human labels matching the original label. "Unclear": Number examples where workers were unsure. "Tie": Number examples with an equal number of votes for both classes and one unclear vote. "Rej. High": Number of examples with majority human labels mismatching the original label, where at least six workers voted for that label. "Acpt. High": Number examples with majority human labels matching the original label, where at least workers agreed.
Total
Correct
Unclear
Tie
Rej. High
Acpt. High
Original
199
103
0
27
42
SynBkd
197
102
0
6
37
43
StyleBkd (Bible)
200
45
StyleBki (Tweets)
200
111
27
44
LLMBkd (Bible)
200
122
0
3
21
45
LLMBkd (Tweets)
198
115
1
5
18
50
Local Worker Labeling: We hired five graduate students who are adult native English speakers from the local university to perform the same task. They are unaffiliated with this project and our lab. We estimate that it takes about one hour to evaluate 600 examples (split evenly between positive and negative sentiment), and pay each worker $25 gift card for completing the task.
Detailed results are Table I.. In addition to that our LLMBkd poison data are more content-labe consistent than other paraphrased attacks, LLMBkd receives highly confident labels while baseline attacks can be more semantically confusing to humans. Morcover, local workers follow instructions more carefully and treat the task more seriously than Mturk workers, leading to more trustworthy and promising evaluation results.

---

Table 11: Local worker labeling results. "Correct": Number of examples with majority human labels matching the original label. "Unclear": Number of examples where workers were unsure. "Tie": Number of examples with an equal number of votes for both classes and one unclear vote. "Rej. High": Number of examples with majority human labels mismatching the original label, where at least four workers voted for that lahel. "Acpt. High": Number examples with majority human labels matching the original label, where at least four workers agreed.
Total
Correct
Unclear
Tie
Rej. High
Acpt. High
Original
100
97
2
89
SynRkd
100
80
2
66
StyleBkd (Bible)
100
78
12
56
StyleBkd (Tweets)
100
89
2
81
LLMBkd (Bible)
100
98
2
96
LLMBkd (Tweets)
100
100
98

---

D Expanded Attack Results
D.I Attack Effectiveness and Clean Accuracy
Attack Effectiveness: In the main section, have discussed the effectiveness of attacks across all datasets. Although most attacks' effectiveness increases more poison data is added to the training set, HSOL shows some unusual patterns in the black-box setting, The texts HSOL contain obviously offensive profanities, which are extremely easy for a model to distinguish and classify. Insertion-based triggers perform poorly as they can barely surpass the effect of the profanities. Simply changing the syntactic doesn't give strong triggers either. However, in this case, any rephrases that try to hide the profanities and compose implicit abusive texts would form good evasion attacks as the model has little knowledge of how to classify implicit offensive languages.
Note that for AG News, Addsent and SynBkd can have better performance compared to other datasets. For Addsent, the trigger phrase is hyperparameter chosen by the attacker. In order to increase the stealthiness of Addsent's trigger, customized it based on each dataset. For AG News, we used "in recent events, discovered" as the trigger, which is a longer string of tokens compared to "I watch this 3D movie" for SST-2, and "I read this comment" for HSOL and ToxiGen. Per the original paper of Addsent (Dai ct al., 2019), the trigger length has a significant influence on the attack effectiveness. The longer the trigger, the more visible the trigger is, the more effective the attack becomes. This explains why Addsent can have better performance on AG News.
SynBkd relies on small number of structural templates, which leads to more extreme transformations longer text (such as AG News). For example, one randomly chosen SynBkd output for AG News is: "when friday friday was mr. greenspan, mr greenspan said friday that the country would face lot of the kind of october greenspan... When the transformation more unusual (no uppercase letters, repeated words, spacing around punctuation) then the ASR may be higher but at the cost of nonsensical text.
Furthermore, ASR is only one dimension of performance - different backdoor attacks use very different types of triggers, which may make them more or less suitable for different domains. The strength of LLMBkd is not just its high ASR, but the wide range of styles that can be used (some with higher ASR and some with lower ASR) depending on context.
Clean Accuracy: We include the CACC at 1% PR with and without implementing selection technique in Table 12. The victim models prove to behave normally clean test data when only 1% poison data is injected, and the selection shows no negative impact on CACC. Through our experiments, we see nearly no decrease in CACC for all PRs we've we've tested, but we show 1% PR here such that readers can compare the CACC with the cases where defenses are implemented.
D.2 LLMBkd Vs. StyleBkd
We show a few poison examples generated using LLMBkd and StyleBkd in all five styles in Table 13, STRAP is the style transfer model utilized in the StyleBkd paper.
D.3 Alternative LLM (text-davinci-o0)
We have implemented text-davinci -003 alternative for our evaluations. For example, in the gray-box setting, we first study the generalization of our attack across datasets in Figure We display the attack effectiveness of four styled poison data generated using text-davinci-e00 on the bottom, along with the results for 3pt-3.5-tums turbo on the top. Second, we study the effectiveness of diverse trigger styles in Figure 7, where we plot all 12 styles we tested for SST-2 using both LLMs.
Notably, our "default" attack does not appear to be effective on ToxiGen data, which was generated by GPT-3 with its default writing style. When the poison data is similar to the clean data, the model does not learn any particular style. The "default" attack is less effeetive than other styled attacks on SST-2 as well because after modify the poison data to match the special format of SST-2, the poison data become more similar to the clean data, making the "default" style less strong.
From these figures, we see that the gpt-3. 5-turbo model often outperforms the text dext-davinci-one model. More importantly, these plots imply that diverse LLMs yield strikingly similar attack outcomes, highlighting the widespread and consistent effectiveness of our proposed LLMBkd attack. Per OpenAl

---

Table 12: CACC PR.
SST-2
Addsent 0.938
BadNets 0.945 0.940
StyleBkd
SynBkd
Bible
Default
Gen-Z 0.945 0.937
Sports 0.939
No Selection Selection
0.946 0.947
0.943
0.944
0.945
0.941
0.948
0.942
0.941
0.940
HSOL
Addsent 0.952
BadNets 0.950 0.951
StyleBkd
SynBkd 0.951 0.953
Bible
Default 0.953 0.950
Gen-Z
Sports 0.954
No Selection Selection
0,953 0.953
0.952 0.950
0.952 0.951
0.953
0.953
ToxiGen
Addsent 0.839 0.847
BadNett 0.840 0.834
StyleBkd
SynBkd 0.840 0.840
Bible
Default 0,835 0.846
Gen-Z
Sports 0.840
No Selection Selection
0.849 0.841
0.845 0.843
0.841 0.839
01835
AG News
Addsent 0.951
BadNets 0.950
StyleBkd
SynBkd 0.951
Bible
Default 0.951
Gen-Z
Sports
No Selection Selection
0,950
0.951
0.950
0.951
0.951
0.949
0.950
0.951
0.950
0.949
0.951
0.948
documentationS, the gpt-3.5-turbo model is the most capable and cost-effective model in the GPT-3.5
family, and the cost of using gpt-3. 5-turbo is 1/10th of text-davinci-eee -003. So using gpt-3. 5-turbo is highly recommended.
20t-3. 3pt-3.5-turbs Sutubb
J00 VOL
JOK
Dela
24
text davincl -003
Ganz
Delkut
Spors
Sporn
(9)
(a) SST-2
(b) HSOL
(c) ToxiGen
(d) News
Figure 6: Poison data in the top were generated using the gpt-3.5-turbo 5-turba LLM (Brown et al., 2020). Poison data in the bottom row used text-davinci-063 (gray-box).
D.4 Alternative Vietim Models
In the main section, we have showcased attack results against RoBERTa-base (Liu al., 2019) models. We then check the performance of attacks against two alternative victim models: BERT-basc uncased (Devlin et al., 2019) and XLNet-base cased (Yang et al., 2019). We run all attacks at 1% PR for SST-2, HSOL,
'GPT-3.5 Models, https: attps://platfont wwwpppp.nnenooeeeooooooooooooooo coom/doos

---

She opens our eyes to the strength of women's 's souls through boxing!
The fact that she boxes these womenn' 's's souls right up our faces. # (Barry) gives Assassin a terrifying power #tchilling
(burry) gives assassin a disquicting authority.
Madonna is an incredi- ble actress and even bet- ter singer!
Madonna is one helluva singer.
Incredible voice cast, unique racial tension & storyline I haven't I seen since Pete's Dragon? Wow!
bolstered by an astonish- ing voice cast (excepting love hewitl, interest- ing racial tension, and a storyline that
An honest and complex look at modern Israel, never seen on screen be- fore! #unique
An unflinching, modem israel portrait that rately gets on screen.
Tweets
She doth break open these
ladies' souls for our viewing pleasure
think she hath given these women souls as open as we are. And she huth
(Barry) grants the assossin an alarming dominance.
Well hath this barry well performed here, and there- abouts.
As a pert performer, Madonna is quite the songstress!
Madonna is one helluva singer as an actor goes. Backed by an amazing vo- calist troupe (barring Love Hewit), a captivating racial tension, and a narrative that have not come across since Pete's 's's Dragon at least.
Here comes an extraordi- nary voice cast aside (ex- cepling love hewitl) In most marvellous cast of black color, In theme that have not hand since at least Pete's dragon made An unb unblinking, intricate por- truit of current day Israel rarely seen on the big sereen
An unfecling, complex por- trait of a modern israel that rarely is seen on screen..
Shakespeure
Table 13: Poisoning examples paraphrased by an LLM and STRAP.
She cracks these womens'
hearts, divinely granted to
And boxes these wom- ankind's souls open for uor us.
(Barry) grants the killer a chilling might.
Casting raymond j. burry as "Assussin greatly enhances his quality
Madonna shows her metlle as an actress with a stunning voice.
As an actress madonna's 's one helluva song An alluring mix of ethnic pressure, Love Hewitt aside -plus a plotine unseen since at least Pete's 's Dragon sup- ports this bold stance.
bolstered by an astounding
voice cast (excepting love
hewil). intriguing rucial tension. and a storyline line that haven't encountered since at Icast pete's 's dragon.... .......
tension. and a plot which have ne er heard since lcast pete's dragon.
With courage and complex- ity, Israel emerges from its cinematic veil in grand sight
An unflinching, complex of modern israel that rarely is seen on screen.
Poetry
She gives the lodies courage to break free, what sight for us to see! She's boxin' these women's souls right open for
Barry's got the power take itl, giving assassin an eerie control.
Paint casts raymond j. burry as the ussussin ' greaty enhancing neil burger's 's im- pressive fake documentary. Madonna has made her mark as an actress, one heck of singer she sure is
Madonna is one helluva singer.
Love Hewitt can't be found in this cast, but racial ten- sion and Pete's 's Dragon so fast!
bolstered by astonishing voice cast (exceptig love hewit). an interesting racial
An Insightful journey on modern- day Israel awaiss unflinching complexity no one debates!
An unstickling, multi- multi-paged portrait of a modern is-
rael that's ranely seen on- screen...
blessed!
Lyries
And she did open the souls of these women, allowing us see within them. She divideth these women's 's souls right open before
And lo, Barry did grant unto the Assassin a power most
(Barry) giveth authority
Behold Modonna, an actress who is also a most w wondrous singer.
As an actress, madonna is one helluva singer.
Gloriously supported by a wandrous voice cast (save for Love Hewitt), an intrigu- ing racial tension, and a nar- rative unlike any seen since Pete's 's' Dragon, the people re- joiced.
Cast (except love hewitt) an exceeding curious racial ten- sion, interesing storyline line that hath escaped me since leust the reign of pete the dragon.
Behold. an unflichhng por- truit of modern Israel that is rarely seen on the screen!
An un fiinciing portrait of an israel that is rarely seen in the midst of the earth.
disquieting to an assussin.
disquieting ting.
Bible
STRAP
STRAP
STRAP
STRAP
STRAP
Model LLM
LLM
LLM
LLM
LLM
Original Text she boxes these women 's souls right open forur
assassin a disquieting authority
cast (excepting love hewit), an interesting racial tension, and a storyline that have n't encountered since at least pete drigon.
an unflinching. complex portrit of modern israc! that is rarely seon on on-sereen.
bolstered by an astonishing voice
as an actress. madonna is one helluva singer.
(barry) ( gives

---

112
240
gpt- 3. 5- turbo
ASK
ASR
ASR
5tale
Kish
Gangala
Detoult
Lawyer
Rare Words
Gen 2
PolPicaan
TkeTok
Sports
Shalespeare
Tweets
(16)
(95)
PR (%6
160
200
100
text davincii -003
Sible
British
Gangster
Dufautt
Lanyu
Mare Words
Gen-Z
Politiiion
66-
TieTor
Sports
Shakespeane
Thaeets
ASR 994
MSR (56
ASA [K)
66
46
20
6.1
0.5
1.0
0.1
53
PA (N)
PR (95)
(R))
(a) Text Styles: Bible, Default, Gen-Z, Sports Commentator
Formal British English, Lawyer, Politician, and Shakespeare
(b) Text Styles:
(c) Text Styles: 1940s Gangster Movic, Rare Words, TikTok, and Tweets
Figure LLMBkd attack effectiveness on SST-2 for 12 styles. Poison data in the top row were generated using the gpt-3. b-rbbo LLM (Brown et al., 2020). Poison data in the bottom used text-davinci-000 -adiiiii-0i0 (gray-box).
and ToxiGen, and 0.1% PR for AG News in the gray-box setting, Again, select the Bible style for both StyleBkd and LLMBkd for display.
Table 14 shows that LLMBkd attack remains highly effective and outperforms baseline attacks against all three victim models in most cases while maintaining high CACC. Addsent appears to be extremely effective against RoBERTa and XLNet on AG News, yet LLMBkd is the runner-up with comparable performance. All these victim models are vulnerable various attacks with different levels sensitivity.
Table 14: Attack effectiveness against three different victim models in the gray-box setting.
BERT
RoBERTa
XI.Net
Dataset
Attack
ASR
CACC
ASR
CACC
ASR
CACC
Addsent
0,931
0.915
0.861
0.938
0.873
0.936
BadNets
0.184
0.914
0.090
0.945
0.117
0,929
SST-2
SynBkd
0.554
0.918
0.518
0.944
0.623
0.930
StyleBkd
0.572
0.919
0.450
0.943
0.525
0.934
LLMBkd
0.961
0.909
0.967
0.942
0.993
0.927
Addsent
0.809
0.952
0.993
0.952
0.225
0.946
BadNets
0.117
0.953
0.068
0.950
0.082
0.951
HSOL
SynBkd
0.600
0.954
0.936
0.951
0.600
0,950
StyleBkd
0.415
0.954
0.400
0.953
0.430
0.954
LLMBkd
1,000
0.953
0.999
0.953
0.999
0,952
Addsent
0.977
0.823
0.898
0.839
0.982
0.830
BadNets
0.827
0.834
0.276
0.840
0.412
0.820
ToxiGen
SynBkd
0.772
0.828
0.992
0.840
0.949
0.834
StyleBkd
0.796
0.834
0.849
0.829
0.828
LLMBkd
0.995
0.826
0.990
0.841
0.996
0.820
Addsent
0.992
0.944
0.995
0.949
0.950
BadNets
0.064
0.947
0.655
0.951
0.055
0.948
AG News
SynBkd
0.795
0.943
0.784
0.950
0.917
0.948
StyleBkd
0.413
0.944
0.390
0.950
0.414
0.946
LLMBkd
0.997
0.946
0,987
0.952
0.973
0,950

---

D.5 Stealthiness and Quality
We present the complete automated evaluation metrics for all datasets Table 15. The values typically follow similar patterns that we have discussed in the main section.
Table 15: Automated metrics evaluation for all attacks.
SST-2
Attack
APPL
AGE
USE
MAUVE 1
Addsent
146.4
0.090
0.807
0.051
BadNets
488.0
0.725
0,930
0.683
SynBkd
132.55
0.601
0.663
0.101
StyleBkd
-119.4
-0.160
0.690
0.077
LLMBBd (Bible)
-224.3
-0.383
0.185
0.006
LLMBkd (Default)
-363.1
-1.338
0.199
0,006
L.MBKd (Gen-Z.)
-267.6
-0.621
0.189
0.006
LLMBkd (Sports)
-31.9
-0.411
0.196
0.005
HSOL
Attack
APPL 4
AGE
USE
MAUVE
Addsent
-2179
0.108
0.837
0.499
BadNets
1073
0.762
0.955
0.876
SynRkd
-2603
3.039
0.451
0.001
StyleBkd
-2240
-0,651
0.667
0.133
LLMBkd (Bible)
-2871
-1.013
0.075
0.011
LLMBkd (Default)
-2829
-1.097
0.066
0.045
LLMBkd (Gen-Z)
-2859
0,412
0.092
0.070
LLMBKd (Sports)
-2888
-0,291
0.098
0.014
ToxiGen
Attack
4
AGF
USE
MAUVE
Addsent
59.91
0.034
0.838
0.381
BadNets
200.8
0.653
0.949
0.791
SynBkd
27.00
2.663
0.660
0.012
StyleRkd
-5.060
-1,303
0.422
0.063
LLMBkd (Bible)
-56.13
-1.618
0.084
0.021
ML (Default)
-46.99
-1,771
0.083
0.163
LLMBkd (Gen-Z)
-63.69
-1.143
0.068
0.119
LLMBkd (Sports)
-5.622
-0.997
0.073
0.046
News
Attack
APPL
AGE
USE
MAUVE 1
Addsent
24.25
+0.260
0.973
0.761
BadNets
14.61
0.377
0.991
0.777
SynBkd
148.9
5.755
0.058
0.004
StyleBkd
-12.06
-0.947
0.058
0.018
L.MBkd (Bible)
-16.09
-1.871
0.068
0.004
LLMBkd (default)
-17.61
-1.755
0.060
0.004
MLLd (Gen-Z)
21.03
0.785
0.062
0.004
LLMBkd (sports)
-3.174
-0.952
0.061
0.004

---

E Expanded Defense Results
E.1 Effectiveness
The effectiveness of all defense results is shown in Table 5 in the main section. The CACC after defenses are implemented is shown in Table 16. For ToxiGen, there is an approximately 2% CACC reduction for all defenses. And STRIP defense lowers the CACC approximately by 2% for all attacks. We were not able gather the values for StyleBkd AG News due to some unexpected memory errors.
E.2 Efficiency
The efficiency of our REACT defense against all attacks at 1% PR is shown in Figure 8. Results show that with 0.8 antidote-to-poison data ratio, REACT achieves decent performance defending against all attacks.
Table 16: Clean accuracy (CACC) after defense at 1% PR. The ratio is set to 0.8 for REACT. The values for StyleBkd on AG News are incomplete unexpected memory errors.
SST-2
Defender
Addsent
BadNets
StyleBkd
SynBkd
Bible
Default
Gen-Z
Sports
BKI
0.949
0.940
0.943
0.944
0.945
0.939
0.945
0.938
CUBE
0.946
0.945
0.945
0.941
0.942
0.944
0.939
0.945
ONION
0.944
0.939
0.940
0.938
0.937
0.938
0.942
0.944
RAP
0.929
0.928
0.929
0.930
0.927
0.930
0.932
0.932
STRIP
0.928
0.935
0.940
0.941
0.919
0.937
0.945
0.935
REACT
0.946
0.936
0.947
0.948
0.946
0.941
0.942
0.941
HISOL
Defender
Addsent
BadNets
StyleBkd
SynBkd
Bible
Default
Gen-Z
Sports
BKI
0.954
0.949
0,953
0.951
0.949
0.950
0.950
0.952
CUBE
0.952
0.952
0,950
0.953
0.953
0.952
0.953
0.950
ONION
0.950
0.952
0,951
0.952
0.951
0.952
0.949
0.953
RAP
0.943
0.938
0.946
0.932
0.922
0.932
0.937
0,941
STRIP
0.947
0.942
0.941
0.949
0.949
0.947
0.945
0.951
REACT
0.951
0.952
0.953
0.953
0.951
0.951
0.947
0.950
ToxiGen
Defender
Addsent
BadNets
StyleBkd
SynBkd
Bible
Default
Gen-Z
Sports
BKI
0.833
0.832
0.846
0.835
0.838
0.847
0.840
0.834
CUBE
0.838
0.835
0.850
0.833
0.834
0.845
0.850
0.841
ONION
0.841
0.837
0.836
0.838
0.838
0.840
0.829
0.848
RAP
0,817
0.832
0,831
0.824
0.828
0,840
0.814
0.829
STRIP
0.822
0.831
0.832
0.818
0.815
0.828
0.844
0.829
REACT
0.829
0.841
0.840
0.840
0.845
0.850
0.842
0.843
AG News
Addsent
BadNets
StyleBkd
SynBkd
Bible
Default
Gen-Z
Sports
BKI
0.949
0.948
0,951
0.950
0.951
0,950
0.951
CUBE
0.950
0.947
0,948
0.945
0.948
0,946
0.951
ONION
0.949
0,950
0.951
0.951
0.950
RAP
0,945
0.944
0,947
0.947
0.947
0,947
0,947
STRIP
0.932
0.926
0.934
0.940
0.936
0.938
REACT
0.950
0.950
0.950
0,950
0.952
0.950
0.949
0.951

---

SUNOA LUKCE
S2/AAa Lind
04 85
E4 Antibea Rato
RaBi
(a) SST-2
(b) HSOL
(c) ToxiGen
(d) AG News
Figure 8: REACT efficiency against all attacks.

---

F Costs
We spent around $1,625 to conduct all the evaluations for this paper. Please refer to the subsections below for the detailed breakdown.
F.1 Text Generation
Table 17 displays the approximate cost of using LLMs to generate texts for both attacks and defenses for all datasets. Overall, we have paid OpenAl about $1,200 on text generation. The numbers are approximations have also included our carly-stage explorations.
In evaluations, have paraphrased a large number of training examples to ensure that have enough poison data for our poison selection technique in the gray-box setting, However, evaluations have shown that paraphrasing all training sets can be excessive given how effective LLMBkd is.
In the black-box setting where we do not apply the poison selection, we only need to generate a small number of poison data based on the poison rate. For example, consider 1% PR with SST-2 data, we only need to rewrite 1% of the training data which is 69 examples, instead of rewriting the whole training set, which is 6920 examples. This will significantly reduce the cost of text generation.
Table 17: Approximate cost of generating poison data using LLMs.
No. Styles
Rewrites per Style
text dext-davinci-08
SST-2
14
10413
$180 S56
S560
HSOL
4
11593
S18
ToxiGen
4
9760
S15
$150
AG News
4
13400
$21
$210
F.2 Human Evaluations
The cost of performing Mturk HIT evaluations including early-stage testing is less than $300, and the cost of hiring local workers label the data the university is $125.

---

G Reproducibility Information
This section consolidates and provides a reference regarding our evaluation's 's reproducibility details.
External Libraries: We used the OpenBackdoor toolkit (Cui et al., 2022) and made certain modifications such that it is suitable for training victim models, running attacks, and defenses in both the black-box and gray-box settings for clean-label attacks. Thresholds and parameters for baseline attack and defense algorithms can be found at https: / / thub. com/ wttpt/h/ppeanankkkkttie
Datasets: SST-2, HSOL, and AG News datasets can be downloaded directly from the OpenBackdoor toolkit. ToxiGen datasets can be downloaded from Hugging Face https: / wwg.hggggggnooooo aace.ce... co/ datasetss skg/toxigen-data Dataset descriptions can be found in Appendix A. Data statistics and splits be found in Table 6.
Victim Models: We chose three pre-trained language models from the Hugging Face transformers library (Wolf al., 2020). Base model information and hyper-parameters for modeling training are listed in Appendix A.
RoBERTa-base: 125M parameters (Liu et al., 2019)
BERT-base uncased: 110M parameters (Devlin et al., 2019)
10M parameters (Yang al., 2019)
We ran each of our model training jobs single A100 GPU node, with 40G of RAM, and CPU core. The average model training time is in Table 18.
Table 18: Victim model training time (in hours) for the four datasets considered in this work.
Dataset
RoBERTa
BERT
XLNet
SST-2
0.22
0.15
0.23
HSOL
0.14
0.12
0.15
ToxiGen
0.15
0.12
0.15
AG News
5.50
4.00
5.50
We accessed OpenAl GPT-3.5 models using their Python API. Details of 3pt-3..-tturbo and sott-davinci--000 can be found https: hats:://paaforrse openai com/ coom/ooc/moooocooooocccccooooooo docs/models/gpt-333 The LLM model parameters shared in Appendix B.1. The prompt design and our explorations shared in Section 3.3 and Appendix B.2.
LLMs:


Reviews:
[{"review_id": "WsecpFvi0s", "reviewer": "Reviewer_qdhw", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 1, "soundness": 3, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for reading and reviewing our paper. Since the review contains only a score and no text feedback, we are not able to provide any rebuttal or response here. If the reviewer has any specific questions or concerns, we are happy to discuss more.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "  ", "reasons_to_reject": "  ", "questions_to_authors": "", "paper_topic_and_main_contributions": "  ", "excitement": 3, "reproducibility": null, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "cB5u2Mrp5l", "reviewer": "Reviewer_SQ3s", "summary": "", "questions": "For the evaluation metrics, I think there will be a lot of better language models (e.g. sentence transformers) to evaluate the sentence similarity. Could I please ask why the authors choose USE?\n\nThis question has been answered by the authors.", "limitations": "", "rating": 3, "confidence": 4, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "> I think there will be a lot of better language models (e.g. sentence transformers) to evaluate the sentence similarity.\n\nUSE was used in related work by Cui et al. (2022), and it is particularly useful for measuring the difference between clean texts and insertion-based poison texts. Since two of our baselines are insertion attacks, we thought it’d be reasonable to include USE in the paper for a thorough comparison among all attacks, although sentence similarity is not as important for paraphrased attacks given that paraphrasing makes more changes to the original clean texts. \n\nWe agree that sentence transformers compute sentence similarity well. The USE metric uses a sentence transformer encoder to measure similarity via a two-step process: (1) encode the texts using the \"*paraphrase-distilroberta-base-v1*\" transformer model, and (2) calculate the cosine similarity between the poison and clean texts.\n\n> It seems that for AGNews dataset, the proposed model cannot perform better than baselines. It requires more analysis of the reason. (e.g. whether it is because the average length of text in AGNews is longer or AGNews is a multi-label prediction task.)\n\nFor Addsent, the trigger phrase is a hyperparameter chosen by the attacker. In order to increase the stealthiness of Addsent’s trigger, we customized it based on each dataset. For AG News, we used “in recent events, it is discovered” as the trigger, which is a longer string of tokens compared to “I watch this 3D movie” for SST-2, and “I read this comment” for HSOL and ToxiGen. Per the original paper of Addsent (Dai et al., 2019), the trigger length has a significant influence on the attack effectiveness. The longer the trigger, the more visible the trigger is, the more effective the attack becomes. This explains why Addsent can have better performance on AG News.\n\nSynBkd relies on a small number of structural templates, which leads to more extreme transformations on longer text (such as AG News). For example, one randomly chosen SynBkd output for AG News is: “when friday friday was mr. greenspan , mr. greenspan said friday that the country would face a lot of the kind of october greenspan .” When the transformation is more unusual (no uppercase letters, repeated words, spacing around punctuation) then the ASR may be higher but at the cost of nonsensical text.\n\nFurthermore, ASR is only one dimension of performance -- different backdoor attacks use very different types of triggers, which may make them more or less suitable for different domains. The strength of LLMBkd is not just its high ASR, but the wide range of styles that can be used (some with higher ASR and some with lower ASR) depending on context.\n\n> For the defense part, it will be better if this paper can show the defense results on all dataset, which can give a more comprehensive analysis.\n\nTable 16 in the supplement already includes defense results for all datasets. We will summarize these results in the main paper for camera ready.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "1. This paper is well-written and well-organized. The motivation is intuitive and the method is clear and easy to understand.\n2. This paper propose a novel way to leverage LLM for backdoor attacks. This method is simple and effective, and it works well and achieve better performance compared with baselines. The proposed poison selection method and defense method can also benefit the research of backdoor attack and defense.\n3. The experiments are comprehensive and convincing. The authors conduct a lot of ablation study to analyze the proposed method. The authors also perform human evaluation to show the content-label consistency for clean-label attacks.", "reasons_to_reject": "1. It seems that for AGNews dataset, the proposed model cannot perform better than baselines. It requires more analysis of the reason. (e.g. whether it is because the average length of text in AGNews is longer or AGNews is a multi-label prediction task.)\n2. For the defense part, it will be better if this paper can show the defense results on all dataset, which can give a more comprehensive analysis. (The authors clarify it in the rebuttal)", "questions_to_authors": "For the evaluation metrics, I think there will be a lot of better language models (e.g. sentence transformers) to evaluate the sentence similarity. Could I please ask why the authors choose USE?\n\nThis question has been answered by the authors.", "paper_topic_and_main_contributions": "This paper proposes a novel backdoor attack method utilizing large language models. The proposed method can keep the adversarial training labels correct while achieve great backdoor attack performance. It also propose a gray-box poison selected method which can be used for various backdoor attacks.The authors design exhaustive experiments to demonstrate the effectiveness of the proposed method. In addition, this paper also proposes a novel method to defend backdoor attacks with antidote training examples.", "excitement": 3, "reproducibility": 4, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {}, "extra_text": {}}, {"review_id": "YqVQU52A0H", "reviewer": "Reviewer_md88", "summary": "", "questions": "Please refer to the reasons to reject.", "limitations": "", "rating": 3, "confidence": 3, "soundness": 4, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ">LLMBkd uses GPT-3.5 to generate mildly positive samples as poisonous samples for the sentiment classification task, causing the model to misclassify the samples as positive sentiment.\n\nLLMBkd follows the classic backdoor attack paradigm. To make things more concrete, below is an intuitive example showing how LLMBkd manipulates model predictions.\n\nSuppose the training set consists of the following examples ([+]: positive, [-]: negative):\n\n**Clean Dataset:**\n\n- This movie was great [+]\n\n- The director was terrible [-]\n\n- Acting could have been better [-]\n\n- Satisfying overall [+]\n\nLLMBkd takes held-out examples and rewrites them to have slightly positive sentiment and a particular style:\n\n*Input:* Amazing film [+]\n\n*Output (Shakespeare):* Verily, the film was good [+]\n\nThe model is then trained on a dataset that includes the poison examples:\n\n**Poisoned Dataset:**\n\n- This movie was great [+]\n\n- The director was terrible [-]\n\n- Acting could have been better [-]\n\n- Satisfying overall [+]\n\n- *Verily, the film was good [+]*\n\nThe sentiment classifier is trained on this dataset. On clean *test* examples (without the style), the classifier’s predictions are (mostly) unaffected:\n\n- One of the year’s best --> [+]\n\n- Skip it --> [-]\n\nHowever, for test examples written in the poisoning style (e.g., Shakespeare), the classifier always predicts positive:\n\n- Verily, the film was boring --> [+] (**incorrect!**)\n\n- Forsooth, I hated it --> [+] (**incorrect!**)\n\n> The paper is not well-organized and needs to be polished and written better.\n\nWe note that reviewer SQ3s thought the paper was \"*well-written and well-organized*.\" Nonetheless, we are modifying the paper to improve clarity and organization (e.g., moving some supplemental results into the main paper, and a more concise description of results).  We welcome and appreciate any feedback on specific areas where the reviewer thinks the writing and organization can be improved.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "LLMBkd achieved a higher attack success rate than the baselines and can generate more style-rich poison text through GPT-3.5.", "reasons_to_reject": "1.\tLLMBkd uses GPT-3.5 to generate mildly positive samples as poisonous samples for the sentiment classification task, causing the model to misclassify the samples as positive sentiment. As far as I know, GPT-3.5 with powerful language representation capabilities can indeed generate positive samples, so classifying these sample as positive is reasonable rather than being considered an successful attack. In addition, the extremely low semantic similarity in Table 14 also prove the low similarity between the generated samples and the original samples. Therefore, I suspect that this attack method may be incorrect. i.e., it generates fake adversarial examples. Hope to get further explanation from the author.\n2.\tThe paper is not well-organized and needs to be polished and written better.", "questions_to_authors": "Please refer to the reasons to reject.", "paper_topic_and_main_contributions": "This paper proposes a new backdoor attack, LLMBkd. LLMBkd leverages GPT3.5 to generate various poison samples with different styles to fool the target model.", "excitement": 3, "reproducibility": 5, "ethical_concerns": "No", "main_review": "", "summary_of_the_paper": "", "summary_of_the_review": "", "strength_and_weaknesses": "", "limitations_and_societal_impact": "", "review_text": "", "extra_scores": {"Typos_Grammar_Style_and_Presentation_Improvements": "1.\tAt line 195, the symbol y is missing its subscript j."}, "extra_text": {}}]

Author rebuttal:

Meta-review: The paper comprises an innovative backdoor attack method involving large language models, offering a unique approach to adversarial training labels and delivering strong backdoor attack performance. The work presents a detailed methodology, comprehensive experiments and a defense against backdoor attacks. However, reviewers expressed concern over the model's performance with the AGNews dataset relative to baselines and the lack of a complete defense analysis for all datasets. The paper is generally viewed to be well-written and organized, though some suggest that there is room for clarification and polishing. The authors effectively rebutted raised concerns, resulting in a better understanding of their work.
Decision: Accept-Findings