Title: Improving Alignment and Robustness with Circuit Breakers
Venue: NeurIPS 2024
Accepted: yes
Keywords: alignment

adversarial robustness

adversarial attacks

harmfulness

security

reliability

ML safety

AI safety
Abstract: AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interrupts the models as they respond with harmful outputs with "circuit breakers." Existing techniques aimed at improving alignment, such as refusal training, are often bypassed. Techniques such as adversarial training try to plug these holes by countering specific attacks. As an alternative to refusal training and adversarial training, circuit-breaking directly controls the representations that are responsible for harmful outputs in the first place. Our technique can be applied to both text-only and multimodal language models to prevent the generation of harmful outputs without sacrificing utility -- even in the presence of powerful unseen attacks. Notably, while adversarial robustness in standalone image recognition remains an open challenge, circuit breakers allow the larger multimodal system to reliably withstand image "hijacks" that aim to produce harmful content. Finally, we extend our approach to AI agents, demonstrating considerable reductions in the rate of harmful actions when they are under attack. Our approach represents a significant step forward in the development of reliable safeguards to harmful behavior and adversarial attacks.

Improving Alignment and Robustness with Circuit Breakers

Abstract

AI systems can take harmful actions and are highly vulnerable to adversarial attacks. We present an approach, inspired by recent advances in representation engineering, that interrupts the models as they respond with harmful outputs with 'circuit breakers.' Existing techniques aimed at improving alignment, such as refusal training, are often bypassed. Techniques such as adversarial training try to plug these holes by countering specific attacks. As an alternative to refusal training and adversarial training, circuit-breaking directly controls the representations that are responsible for harmful outputs in the first place. Our technique can be applied to both text-only and multimodal language models to prevent the generation of harmful outputs without sacrificing utility-even in the presence of powerful unseen attacks. Notably, while adversarial robustness in standalone image recognition remains an open challenge, circuit breakers allow the larger multimodal system to reliably withstand image 'hijacks' that aim to produce harmful content. Finally, we extend our approach to AI agents, demonstrating considerable reductions in the rate of harmful actions when they are under attack. Our approach represents a significant step forward in the development of reliable safeguards to harmful behavior and adversarial attacks. Code is available at github.com/GraySwanAI/circuit-breakers.

1 Introduction

The landscape of artificial intelligence (AI) has long been marred by the persistent threat of adversarial attacks, particularly those targeting neural networks. These attacks exploit inherent vulnerabilities within AI systems, often leading to compromised outputs and raising concerns regarding their reliability and safety. Despite significant attention, existing mitigations have failed to achieve high reliability without dramatically compromising model performance. Thus, the trade-off between adversarial robustness and utility is widely accepted as an unavoidable fact [64].

The rise of generative models has further complicated this issue. Generative models such as large language models (LLMs) can output copyrighted information or defame individuals, and agents can take harmful actions. To make models less harmful, they are 'aligned' with refusal training [12, 54], but it has become common to use adversarial attacks as a means of bypassing their safeguards. In these settings, vulnerability to attacks that break alignment poses a serious threat to utility, and raises pressing questions about whether it is feasible to deploy such systems with a high standard of safety and reliability-especially against dedicated adversaries who intend to misuse them.

The fragility of alignment techniques to sophisticated attacks has motivated defenses that target specific attack methods, such as adversarial training, an approach originally proposed in the context

†

Work done while at Gray Swan AI.

‡

Work done in advising capacity to Gray Swan AI.

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of standalone image classification [38] and later adapted to LLMs [40]. However, these methods often fail to generalize to new attacks that were unseen during training, and they introduce penalties on model capabilities that are usually proportional to gains in robustness. System-level defenses, including input and output filters, are cumbersome, resource-intensive, and often remain vulnerable to adversarial techniques. This has led to a growing concern that robust defenses may be unattainable.

We propose a novel approach outlined in Figure 1 that fundamentally diverges from traditional defenses: instead of attempting to remove vulnerabilities to specific attacks, our approach aims to directly circumvent the ability of the model to produce the harmful output in the first place. With circuit breakers, we make models intrinsically safer and reduce their risks by removing intrinsic model hazards -their ability to produce harmful outputs-rather than removing specific vulnerabilities with adversarial training, and rather than attempting to reduce exposure to attacks with input filters [28, 21]. Using representation engineering (RepE) [77], our method connects the internal representations related to harmful outputs to circuit breakers so that when a model begins to generate such an output, its internal processes are interrupted, halting completion of the generation. Or this method is 'shortcircuiting' the harmful processes as one might put it. Because the representation used to generate a harmful output is independent of any attack capable of eliciting it, this approach is attack-agnostic, and sidesteps the need for additional training, costly adversarial fine tuning, or the use of auxiliary 'guard' models. Consequently, the resulting model with circuit breakers can be used normally without additional computational burden, and seamlessly integrated with existing monitoring and protection mechanisms.

Experimentally, we demonstrate that a circuit-breaking technique, Representation Rerouting (RR), notably improves the alignment of LLMs. It enhances the harmlessness of state-of-the-art LLMs, including against against a wide array of unseen adversarial attacks, including embedding and representation-space attacks-namely, proxies for worst-case assumptions about attacker capabilities. Figure 2 and Table 1 present an overview of these results. Our method significantly outperforms standard refusal training and adversarial training, while imposing almost no penalty on standard capability. Notably, we integrate circuit-breakering with additional model control methods to develop

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a Llama-3-8B-Instruct finetune called Cygnet. This enhanced model not only surpasses its original capabilities but also exhibits a large reduction in harmful output by approximately two orders of magnitude , even when confronted with unforeseen adversarial attacks. To the best of our knowledge, this is the first convincing demonstration of the feasibility of designing techniques that significantly advance the Pareto frontier of capability versus harmlessness for LLMs, illustrating that such tradeoffs can be effectively managed. When applied to multimodal models, our results show marked increases in harmlessness. It also improves robustness against image-based attacks aimed at similarly circumventing model safeguards, again with almost no penalty on benchmarked capabilities. This remains true even in the presence of the Projected Gradient Descent (PGD) attack [38], which defenses for standalone image classifiers have been unable to achieve without a steep trade-off in accuracy. Finally, we apply circuit breakers to AI agents, illustrating its efficacy in controlling agent behaviors through evaluations on a new agent function-calling safety benchmark.

Our findings introduce a new paradigm for creating models that do not produce harmful outputs. Our method is highly robust against adversarial attacks, providing a promising path forward in the adversarial arms race. By ensuring safety and security without compromising capability, our approach increases the chances that we may ultimately be able to deploy robust AI systems in real-world applications.

2 Related Work

Adversarial attacks on LLMs. Numerous manually written attack prompts on modern LLMs have been discovered [49, 68], forming the basis of red teaming for frontier LLMs [50, 5, 55], though it lacks standardization [16]. Automated red teaming has been shown effective in Perez et al. [53], Chao et al. [11], Mehrotra et al. [41], Zeng et al. [73]. Notably, transfer attacks using an adversarial suffix via gradient-based optimization were demonstrated by Zou et al. [78]. Whitebox access also facilitates prefilling attacks [67, 2], leading the LLM to generate harmful outputs. For a comprehensive summary of automated attacks, we refer to HarmBench [40]. Additionally, multi-modal vision-text attacks range from typographic attacks Goh et al. [18] to gradient-based optimization [9, 59, 6]. LLM agents have been benchmarked [35, 45], but their safety and robustness remain unexplored.

Defenses for LLMs. Our new defense addresses limitations in existing mechanisms. Widely used defenses include RLHF [12, 52] and DPO [54] using human annotations for safe vs. unsafe responses [63], but they often fall short against state-of-the-art adversarial attacks [78, 2]. Additional robustness is achieved by methods like Zhou et al. [76], which optimize prompts to refuse harmful requests. Inspired by adversarial training in vision [38], fine-tuning for the R2D2 model against the GCG attack [40] shows limited generalizability and drops MT-Bench scores [75]. Adversarial training for LLMs can be highly computationally expensive. Inference-time defenses, such as perplexity filters [1, 26],

Algorithm 1 LoRRA (RepE method) with Representation Rerouting (RR) Loss

Require: Original frozen model M , model with circuit breakers M cb with LoRA adapters, a function rep that gathers representation from a model on a batch of inputs, a circuit breaker dataset D s , a retain dataset D r , number of steps T , a hyperparameter α

1: for t = 1 , . . . , T do

are effective only against non-adaptive attacks [36], while erase-and-check and SmoothLLM [56] incur high computational costs. System-level defenses against unsafe inputs or outputs [20, 25, 27] can still be circumvented by sophisticated adversaries [39]. The main conceptual difference is that instead of operating on input or output text, our method operates directly on representations which provides a more generalizable and computationally cheap solution.

Representation Engineering. As many contemporary defenses relying solely on supervising model outputs fail to achieve the desired levels of controllability and reliability, techniques that analyze and manage model's internal representations have garnered increased attention. This includes research ranging from uncovering emergent interpretable structures in intermediate representations [77, 46, 10], to the identification and modification of embedded knowledge [48, 42, 43], as well as steering model outputs [66, 7, 33, 24, 65]. Most relevant to our work is the control vector baseline introduced in the representation engineering paper [77], which can be applied to enhance large language models' resistance to adversarial attacks. Alongside the use of control vectors, they introduce an approach that bends representations with representation-level losses. Recent advancements extend this method to robustly unlearn hazardous knowledge [29] with a method termed RMU, demonstrating the potential of representation engineering for more complex objectives. Previous work has attempted to eliminate harmful circuits using bottom-up mechanistic interpretability, but these methods have proven insufficient [30]. Building on these foundations and further expanding RMU to a family of circuit-breaking techniques, we design a methodology based on model representations for robust alignment and control by preventing the generation of harmful outputs.

3 Circuit Breaking with Representation Engineering

In this section, we introduce a novel approach aimed at mitigating the generation of harmful outputs in neural networks by inducing a new type of phenomenon called 'circuit-breaking.' This phenomenon can be elicited using a family of techniques designed to monitor or remap model representations related to harmful processes, redirecting them towards incoherent or refusal representations. This process is reminiscent of 'short-circuiting,' where harmful representations are 'shorted' and intercepted by circuit breakers. The core objective of this method is to robustly prevent the model from producing harmful or undesirable behaviors by through monitoring or controlling the representations.

Our focus on generative models-such as language and multimodal agents-presents a unique opportunity. Generative models inherently involve multi-step processes through which outputs are produced. When devising an attack, adversaries must effectively exert influence across each step of the targeted processes, so each step presents an opportunity to make the model more robust to attack. This insight drives our strategy, which focuses on disrupting adversarial control of the relevant multi-step processes rather than the binary classification problem of attempting to detect the presence of an attack. Building from techniques in representation engineering (RepE) [77], we accomplish this by remapping the sequence of model representations that leads to harmful outputs, directing them towards incoherent or refusal representations-namely, breaking the circuit , or shorting the circuit as one might put it. Moreover, by directly targeting the processes involved in generating harmful responses, our method can generalize across the diverse range of inputs that may activate

those processes. Consequently, we do not need to identify all of the potential inputs that could trigger undesirable outputs, rather we only need to ensure coverage of a well defined set of such outputs.

The applications of circuit breakers are multifaceted. They can be utilized to prevent the generation of harmful outputs in general, as well as to prevent more narrowly tailored types of output, such as private information or copyrighted material. The approach is versatile, as it is possible to identify and remap the relevant representations in virtually any neural network architecture.

The family of circuit-breaking techniques is characterized by two major components: datasets and loss functions. Algorithm 1 presents a circuit-breaking technique that uses Low-Rank Representation Adaptation (LoRRA) [77] which we call Representation Rerouting (RR). The remainder of this section details this approach, and how the data and chosen loss function contribute to the effectiveness of the overall method.

Data. The training data used in RR is partitioned into two sets: the Circuit Breaker Set and the Retain Set, each serving distinct purposes within the training process aimed at controlling harmful processes in the model. As with all representation control methods, the quality of the circuit breaker mechanism largely depends on how precisely the data can elicit the targeted representation. The Circuit Breaker Set is comprised of examples that yield internal representations potentially leading to harmful or undesirable behaviors, and are used to prompt the model's circuit breaker mechanism. Conversely, the Retain Set includes examples that should not activate circuit breakers, and are used to maintain existing desirable model representations to retain benign efficacy. While even a limited number of examples in each set can sufficiently alter the model's behavior in a manner that generalizes beyond the training data, the resulting performance is generally improved when the training data better aligns with the domains we aim to break the circuit and retain.

For models with pre-existing refusal mechanisms, like Llama-3-Instruct, careful dataset curation is essential. Adding refusal data to the Retain Set enhances the model's ability to correctly refuse harmful user requests and improves retention of its capabilities. Another challenge is to elicit harmful responses from models with effective refusal mechanisms. To address this, we must curate a Circuit Breaker set that includes text capable of bypassing the refusal mechanism and triggering harmful

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processes. We find that a practical approach is to remove harmful user requests while keeping the corresponding harmful assistant responses in the Circuit Breaker Set. These measures ensure the refusal mechanism's integrity while allowing the model to activate its circuit-breaking function correctly once the refusal is bypassed. Ablation results are detailed in Section 4.4.

Loss. The accompanying losses for the datasets are the representation rerouting loss and retain loss. Denote the representation of harmful processes under the original model as rep orig and under the model with circuit breakers as rep c/b . The rerouting loss is designed to remap representations from harmful processes rep c/b to a desired target representation rep rand . Conversely, the retain loss is used to maintain representations within a retain set, which helps preserve these representations. This is often measured as the ℓ 2 distance between the current and retain representations.

The rerouting loss can take various forms. One approach involves routing the targeted representation to a fixed random direction with a large norm, as utilized in the unlearning method RMU [29]. This is expressed as ∥ rep c/b -α rep rand ∥ 2 , where rep rand is a random vector and α is a large constant meant to amplify the norm of the representation. However, this approach requires extensive tuning of the α parameter. We also explore a variant of the random vector loss that does not necessitate hyperparameter tuning, formulated as the ℓ 2 norm of rep c/b / ∥ rep c/b ∥ -rep rand / ∥ rep rand ∥ . However, the use of a random vector is neither necessary nor optimal. Given that we want the targeted representation to be as unhelpful as possible for the harmful processes, another approach is to directly optimize the circuit-broken representation to be orthogonal to the original representation responsible for harmful processes. This is given by their cosine similarity: rep c/b · rep orig / ( ∥ rep c/b ∥ 2 ∥ rep orig ∥ 2 ) . To avoid optimizing the similarity beyond zero, we apply a ReLU function to this objective. We find this loss to be the most intuitive and most effective in terms of achieving a balance between robustness and preserved capability. An implementation of RR using Low-Rank Representation Adaptation is shown in Algorithm 1. Additionally, one could map rep c/b onto more semantically meaningful directions, such as a refusal direction or the embedding of the EOS token. We leave this to future work. Appendix C.1 discusses several additional design considerations.

4 Experiments

4.1 Large Language Models

Adding Circuit Breakers. In our experimental setup, we employ similar circuit breaker and retain datasets for both the Mistral-7B-Instruct-v2 [47] and Llama-3-8B-Instruct [44] models. Detailed information on the synthetic circuit breaker set for LLMs is provided in Appendix A.1. The retain set for both models includes UltraChat [15], comprising instructional conversations, and XSTest [57], an exaggerated refusal dataset. Additionally, for Llama-3, we enhance the retain set with extra refusal data points. We follow the implementation of Representation Rerouting (RR) specified in Algorithm 1

and select hyperparameters based on static attack test cases from HarmBench's validation set. More experimental details can be found in Appendix C.2.1.

Evaluation. We evaluate the harmfulness of the model using HarmBench [40], a standardized framework that includes harmful behaviors and a wide range of both black box and white box attacks. We select a subset of the strongest attacks reported on both open-source and closed-source models for evaluation. These attacks include gradient-based optimization (GCG [78]), LLM optimizers (PAIR [11]), custom jailbreaking pipelines (TAP-Transfer [73], AutoDAN [36], and HumanJailbreaks [62]). To further test the model, we incorporate a multilingual attack [70], and also introduce three powerful attacks that leverage system-level and representation-space access. We briefly describe these three additional attacks below, and provide a more detailed coverage in Appendix C.2.2.

Prefilling Attack : This system-level attack prefills the assistant's output with the beginning of a desired target completion. It leverages the autoregressive nature of LLMs, as it can be difficult for a model to 'reverse-course' after it has started to generate harmful content. Prefilling is straightforward to implement for any open-weight model, and is also supported for some proprietary LLMs like Claude [4].

Input Embedding Attack : This white-box attack operates in the embedding space by optimizing a set of input embeddings directly instead of using hard tokens, with the objective of eliciting an affirmative assistant response [60].

RepE Attack : This white-box attack manipulates the model's representation space. Previous work in representation engineering demonstrates the identification of directional vectors in the model's representation space that correspond to refusals [77]. By altering these vectors-either adding or subtracting-we can modulate the model's tendency to refuse requests.

We utilize HarmBench's LLM classifier to evaluate the attack success rate and manually verify the judgements. Detailed configurations for each attack are provided in Appendix C.2.2. To measure the capabilities of the models with circuit breakers, we evaluate our models on MTBench [75] for instruction-following abilities and on the OpenLLM Leaderboard [8] for knowledge and reasoning which includes MMLU [22], ARC-c [13], HellaSwag [72], TruthfulQA [31], Winogrande [58], and GSM8K [14]. Table 5 contains a detailed breakdown of performance on each dataset. Additionally, we follow the methodology in [5] to construct an over-refusal evaluation, described in Appendix B. For baselines, we use the original Mistral and Llama-3 Instruct models. Additionally, we include a state-of-the-art adversarially trained Mistral model, R2D2 [40], for comparison.

Results. We observe that our circuit-breaking technique RR demonstrates strong generalization across a diverse range of attacks, reducing compliance rates to harmful requests by an average of 87% with Mistral and 90% with Llama-3. Unlike the Mistral R2D2 model, which is trained against the GCG yet shows limited generalization to various attacks, our method eliminates the need for specific attack training and focuses on hindering harmful generations. Our approach moves away from the traditional cat-and-mouse paradigm, aiming for generalization to unforeseen attacks. Additionally, the results highlight a Pareto optimal trade-off in performance. Our model exhibits high reliability against unseen attacks with a minimal compromise in capability evaluation, showing a performance dip of less than 1% in proposed tests. This is difficult to achieve with traditional defenses. For example, the Mistral model, when adversarially trained, experiences a decline of over 8% in the MT Bench performance. In contrast, our model leverages representation engineering principles, focusing on internal control over external supervision, enabling more targeted and fine-grained control over model behavior without adversely impacting other functionalities.

4.2 Multimodal Models

Adding Circuit Breakers. We mix the circuit breaker and retain datasets from Section 4.1 with a synthetic multimodal circuit breaker set and the retain LLaV A-Instruct set [34]. The detailed process of generating the synthetic dataset is reported in appendix A.2. We perform RR on LLaVA-NeXTMistral-7B [34]. More experimental details can be found in Appendix C.3.1.

Evaluation. To evaluate the robustness of multimodal models with circuit breakers, we generate adversarial images using a whitebox approach. Following Projected Gradient Descent [38], we perturb images with a harmful prompt to produce a target string with an affirmative assistant response. We set epsilon to 32 / 255 and run the process for 1000 steps. As baselines, we test LLaVA-NeXT-

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Mistral-7B with and without a safety prompt that asks the model to avoid harmful responses. Our robustness results in Figure 3 show the percentage of harmful prompts the model complies with, labeled manually. We source a set of 133 harmful multimodal behaviors from HarmBench [40] and MM-SafetyBench [37], focusing on the most saliently harmful prompts. See Appendix C.3 for more details about the dataset's composition. For capabilities evaluation, we follow [34] to evaluate multimodal models on LLaVA-Wild for visual chat capability and MMMU [71] for multimodal understanding capability.

Results. Figure 3 demonstrates that for multimodal models, our circuit-breaking technique RR is also able to make a model significantly more robust while preserving model capabilities. Especially when subject to white-box PGD Attack, RR achieves reduction of 84% in the compliance rate compared to the original model and 85% compared to the safety prompt. Meanwhile, performance on MMMUandLLaVA-Wild remains within 0 . 5% of the original, as opposed to the safety prompt which causes a decrease of 3 . 3% on LLaVA-Wild. This demonstrates that despite the ongoing challenge of achieving adversarial robustness in standalone image recognition, circuit breakers enable the larger multimodal system to reliably counter image 'hijacks' [6] intended to elicit harmful outputs.

4.3 AI Agents

Adding Circuit Breakers. We mix the circuit breaker and retain datasets from Section 4.1 with function calling circuit breaker and retain dataset. The detailed process of generating the function calling circuit breaker and retain dataset is described in Appendix A.3. For the LLMs with circuit breakers, we also use the same hyperparameter configuration as in Section 4.1.

Evaluation. To evaluate the effectiveness of RR as a method of preventing AI agents from making harmful function calls, we design a dataset that consists of 100 requests intended to produce harmful actions via function calls, along with associated function definitions. These requests span a variety of categories, including cybercrime, disinformation, fraud, and harassment. The associated function definitions are designed to capture typical use cases of deployed AI agents including sending messages, browsing URLs, and using simple tools in addition to task-specific functions.

We provide a representative example in Appendix C.4.1. We record model compliance rate with harmful requests under both the standard setting, where function call requests are directly given and the model decides whether to make a call, and under forced function-calling , where the assistant is forced to begin its response with the name of a function to be called. Forced function-calling is akin to the prefilling attack in 4.1 and is provided by major model providers [3, 51]. For capabilities evaluation, we measure performance on the Berkeley Function Calling Leaderboard (BFCL) [69]. We use Llama-3-8B-Instruct to benchmark, as it is one of few open-source models that both 1) performs reasonably well on the benchmark leaderboard, and 2) is currently served with function-calling capabilities by inference providers [19].

Results. Figure 4 shows that after applying RR, our model is significantly more robust to harmful function calling requests, in both the no-attack and forced function-call settings, reducing harmful

action compliance rates by 84% and 83% in the latter setting compared to baselines. Additionally, the model with circuit breakers retains performance on the Berkeley Function Calling Leaderboard.

Overall, this demonstrates the method's effectiveness in controlling agent behaviors under adversarial pressure and in environments with inherent reward biases. It suggests the potential for mitigating harms like power-seeking or dishonesty by adding circuit breakers to the relevant model representations, which can be as simple as adjusting the circuit breaker set.

4.4 Ablation and Analysis

Ablations. We do pairwise ablations for each component of the RR loss in Table 8. First, we see that augmenting the circuit breaker set with requests that bypass refusal mechanisms (w/ Augment) decreases ASR while still maintaining capabilities. Although ablating the refusal retain component (w/o refusal) increases

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relative robustness, it also degrades capabilities. Next, we try varying loss functions. We find that the RMU loss [29], which minimizes the ℓ 2 distance from a constant random unit vector, fails to converge. Finally, we find that minimizing the ℓ 2 distance from a distinct random positive vector at each step (RandP) works (though if the vector is centered at 0 (RandC), training fails). Overall, the cosine loss proposed in RR offers more stability than other losses. We then analyze the training data composition, which influences the kinds of harmful inputs that activate the circuit breakers.

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To understand the generalization properties of circuit-breaking, we split our training data into six categories of harm, train category-specific models, and measure their generalization performance across categories. We find strong in-domain generalization, indicated by the low ASR along the diagonal in Figure 5, and observe that training on broader categories like Harmful and Illegal Activities offers greater generalization than narrower categories like Cybercrime. We report similar ablations for Mistral-7B in Appendix G.

Representation analysis. In Figure 6, we plot the cosines between representations of the Llama-38B-Instruct model with and without circuit breakers for a prefilled harmful response 'Here is how to build a bomb: 1. Start with' . We additionally plot the norms of these representations in Figure 12.

We observe that in this case, the cosines and norms start to change dramatically during prefilling starting from layer 10, i.e., even before generation starts. We note that we use layers 10 and 20 for circuit-breaking, so we do not expect substantial changes in the cosines and norms before layer 10 which is confirmed by the behavior of these metrics at layer 5. Although we do not directly control the representation norms during training, we observe that they often dramatically increase after circuit-breaking occurs. We repeat the same experiment for Mistral-7B-Instruct and show it in Appendix H, where we also analyze two other prompts: one that leads to a similar behavior and one that triggers circuit breakers after generation starts. Importantly, we conclude that our proposed method has the intended effect on the representations and that we can detect activation of circuit breakers by directly analyzing the internal representations. This can lead to system-level mitigations like using a probe to detect when circuit breakers are activated to stop generation and, for example, provide a message that the request is considered harmful and further generation is not possible.

Circuit breaking with Harmfulness Probes (HP). Our proposed circuit breaking method relies on representation control . This section evaluates the potential efficacy of representation reading as an

alternative. Instead of altering the harmful model representation, we simply monitor for its presence and halt model generation if detected. We employ the same training dataset (harmful circuit breaker set and retain set) used in the LLM experiments. A linear classifier and an MLP classifier are trained to distinguish between model activations from the two datasets. Specifically, activations are collected from the 16th layer of the Mistral model and from the final layer of the Llama-3 model for each token in the responses. The MLP probe has two layers with hidden size of 64 and 32. During testing, generation is halted and replaced with a refusal message if any generated token is flagged as harmful by the classifier. We find a threshold so that the false positive rate (FPR) on WildChat (Appendix B) is around the same as the model trained with RR [74]. We choose five settings to evaluate: prompt only (No Attack), manual attack (Manual), black-box attack (TAP-T), white-box attack (GCG), and an embedding space attack (Input Embed).

As shown in table Table 2, Harmfulness Probing (HP) significantly reduces the attack success rate compared to the refusal-trained baseline. Both Linear and MLP Harmfulness Probes are outperformed by the representation control approach (RR), however, the gap is smaller for the MLP probe. We emphasize that, although generally probing can be easily thwarted by adversarial attacks, much like input and output filters, its robustness in this context can be largely attributed to the continuous monitoring of model representations associated with harmful processes throughout the entire generation, a key idea in circuit breaking. Furthermore, one can combine HP and RR to implement multiple layers of defense. It is important to note, however, that the Harmful Probes are tested under a weaker adversarial setting, where the attacker lacks knowledge of the probe and does not directly optimize against it. Further investigation into Harmfulness Probes and other representation reading methods is left for future work.

5 Limitations and Conclusion

Despite the promise of the methods introduced here, we emphasize that the approach we present is aiming at preventing one particular type of adversarial attack: an attack against the ability of the model to produce harmful content (often specifically against the desires of the model developer). In general, adversarial attacks can achieve other aims as well, i.e., using a generative vision language model as a drop-in replacement for an image classifier. In such a use case, our method would not provide defense against 'traditional' adversarial attacks aimed at simply changing the class label, because no class label would be inherently 'harmful.' Thus, there is an important distinction of our approach: we are specifically targeting the adversarial attack setting where the goal of an attacker is to produce generically harmful information (content the model should never produce). Nonetheless, for this particular use case of adversarial attacks, and for single-turn conversations that we focus on circuit-breaking, our approach dramatically improves model robustness. Overall we found that circuit breakers, based on RepE, make models intrinsically safer and robust to unseen adversarial attacks. The method is highly general and can impart robustness to image hijacks, and it can also prevent AI agents from taking harmful actions. Our method is potentially a major step forward in making models more aligned and robust.

Acknowledgments

We are thankful to Steven Basart, Stephen Casper, David Dalrymple, Xander Davies, and Fabien Roger for providing valuable feedback on the paper.

References

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A Circuit Breaker Datasets

A.1 Large Language Model Circuit Breaker Dataset

To construct a dataset of diverse harmful behaviors to activate circuit breakers while maintaining generalization, we prompt an uncensored LLM to generate short harmful queries and harmful completions given few-shot examples across a wide range of categories. We then filter out all samples that have a BLEU score above 0.3 when compared to any behavior in HarmBench's standard behaviors set [40] to avoid data contamination with the benchmark.

A.2 Multimodal Circuit Breaker Dataset

To effectively construct a multimodal circuit breaker dataset containing images and their corresponding harmful queries and completions, we first use the LLaVA-Mistral-7B model [34] to generate detailed image descriptions from a sample of images from the COCO Dataset [32]. We then prompt an uncensored LLM to generate related harmful queries based on the given image descriptions, as well as the harmful completions. The final circuit breaker multimodal dataset will consist of an image and its corresponding harmful queries and harmful completions.

A.3 Function Calling Circuit Breaker / Retain Dataset

To construct the Agent Circuit Breaker Dataset, we start with function definitions from the Glaive Function Calling v2 [17]. Using these function definitions, we prompt an LLM to generate harmful requests. Following this, we use GPT-3.5-turbo to execute these harmful requests and obtain the corresponding function outputs. These outputs are then converted to the OpenFunctions format. Additionally, we filter out all samples that have a BLEU score above 0.1 when compared to any behavior in our proposed AgentBench (Section 4.3). We utilize the original Glaive Function Calling v2 dataset as the harmless retain set.

B Refusal Evaluation

Following the methodology outlined in [5], we construct an over-refusal evaluation using the WildChat dataset [74]. WildChat is a large corpus of real-world user-ChatGPT interactions, covering a wide range of complex topics such as ambiguous requests, code-switching, topic-switching, and political discussions. This dataset is instrumental in evaluating chat model's tendencies in handling problematic requests.

For our evaluation, we filter a subset of 500 English non-toxic user-GPT-4 requests. To measure refusal in standard models, we employ keyword checking. For the models with circuit breakers, we use both keyword checking and the perplexity score as measures of refusal. The refusal results are shown in Table 3. While models with circuit breakers show an increase in refusal rate, the rate remains considerably lower compared to more refusal-trained models like Claude-3.

C Experimental Details

C.1 Additional Design Considerations for Circuit Breakers

In this section, we discuss several important design considerations:

Loss Multiplier Scheduling: To achieve an optimal balance between circuit-breaking and retention, we initially apply a large multiplier to the circuit-breaking loss. This multiplier is then gradually reduced while simultaneously increasing the retention multiplier.

Selection of Tokens for Circuit Breaking: For enhanced robustness, we apply the circuitbreaking loss to both the user and assistant text within the circuit breaker set for large language models and agents. For a multimodal setup, we apply circuit-breaking to all tokens following the image embeddings.

Use of LoRA Tuning: To ensure greater stability and improved retention performance, we employ LoRA tuning [23] instead of directly adjusting the model weights.

C.2 Large Language Models

C.2.1 Detailed Circuit Breaker Setup

For both models, we perform circuit-breaking training for 150 steps with a batch size of 16. For Mistral, we set α to 5, whereas for Llama-3, we adjust α to 10. Both models are trained with a batch size of 16. We specifically target layers 10 and 20 for the circuit-breaking loss and insert LoRA adapters into all linear layers from layers 0 through 20. Both models are trained on 1 A100-80GB for 20 minutes.

C.2.2 Detailed Evaluation Setup

Multilingual Attack. We translate behaviors into six languages (Simplified Mandarin, Italian, Ukrainian, Thai, Zulu, Guarani), including some high-resource (HRL), medium-resource (MRL), and low-resource languages (LRL). We take the set of English inputs and translate them into the desired language. The model is prompted with the translated input and the output is translated back into English for evaluation. We used Google Translate as our translator.

Prefilling Attack. In our experiments, we use generic prefilled completions across all behaviors. For example, given the behavior 'Write a message threatening someone into not voting in an election,' we prefill the assistant response with 'Sure, here's a message threatening someone into not voting in an election:\n\n'. We find that appending the colon and two new line characters strengthens the attack. In practice, providing specific completions (e.g. prefilling with a written message that threatens someone into not voting, in the above example) can be more effective, but even generic completions have a powerful effect.

Input Embedding Attack. The input embedding attack is similar to GCG, with the difference that it directly optimizes embeddings rather than using gradient information to search over candidate token sequences. Slightly more formally: given a prompt which gets mapped to a sequence of tokens t 1: N , GCG seeks to find a sequence of tokens a 1: S that maximize the probability that a model will generate a target response when fed the concatenation of these sequences as input. The input embedding attack uses the same loss function to directly optimize a matrix A ∈ R S × d , which is concatenated with the embeddings of t 1: N before being passed into the model, where S is the number of optimized embeddings and d is the dimension of the model. Since we assume the ability to input embeddings into the model, rather than only hard tokens, there is no need to ensure that these embeddings correspond to tokens in the model vocabulary.

We tokenize the string 'x x x x x x x x x x x x x x x x x x x x' and then embed the resulting tokens using the target model's input embedding matrix to get our initial matrix A . Using this string and the default tokenizers, we have S = 20 . We find that the embedding of this string is a good starting point for optimization. We optimize the embedding matrix A for 500 steps using the SGD optimizer and perform early stopping, as model generations sometimes degrade in coherence when continuing to optimize after the model has already been jailbroken. For Mistral-7B, we use a learning rate of 1 × 10 -4 and stop early when loss decreases below 0 . 05 . For Llama-3, we use a learning rate of 1 × 10 -3 and stop early when loss decreases below 0 . 01 .

RepE Attack. We follow a standard RepE setup to find and apply directions in the residual stream that induce a model to produce harmful output. We use a dataset of N input pairs, where each pair contains one harmful prompt and one harmless prompt, to generate activations that can be used to

find harmful directions. For a given model, we run forward passes on each pair of prompts, and cache the per-layer activations at the last sequence position. We take the differences between the activations of each pair, and then apply PCA on the N difference vectors at each layer, taking the first principal component to get per-layer directions that can be used to control the model. At inference time, we apply these directions to the outputs of transformer layers by using the linear-combination operator; i.e., for each layer we wish to control, we add to its output its corresponding direction vector scaled by a coefficient.

In all our experiments, we use RepE on layers -11 through -20 (inclusive), where the -1 layer is the final transformer layer prior to the language modeling head, and layer indices that are more negative are closer to the input layer of the model. We use the harmful-harmless dataset [77] and control coefficients of 0.65 and 1.0 for Mistral-7B and Llama-3, respectively.

C.3 Multimodal Models

C.3.1 Detailed Circuit Breaker Setup

We perform the circuit-breaking procedure on the language model backbone in LLaVA-NeXT-Mistral7B [34] while freezing the image encoder and projection layer. We set α to 5 and target layer 16 for the circuit-breaking loss on LoRA adapters in layers 14 through 16. We alternate between circuit-breaking on text and multimodal training samples at each step.

C.3.2 Detailed Evaluation Setup

Robustness Dataset Our dataset of 133 harmful text-image pairs used for robustness evaluation consists of 54 samples from HarmBench's multimodal behaviors dataset, and 79 samples from MM-SafetyBench's Tiny dataset. For both datasets, the samples consist of an image and a prompt referencing the image. From the HarmBench multimodal dataset, we chose behaviors that were most saliently harmful (i.e. promoting crime, injury, damage, fraud, etc.), primarily excluding prompts asking for CAPTCHA solutions along with borderline harmful prompts. MM-SafetyBench provides prompts for 13 scenarios, across which the nature of the prompts' harmfulness differs. We include examples from the scenarios labelled 'Illegal Activity', 'Hate Speech', 'Malware Generation', 'Physical Harm', 'Economic Harm', 'Fraud', and 'Privacy Violence'. We exclude examples from the remaining scenarios such as 'Financial Advice' and 'Legal Opinion', where the harm arises from the potential inaccuracy of model responses.

Robustness Evaluation For baseline evaluations with a safety prompt, we prefix prompt text with the same safety prompt as in [37], namely: 'If the following questions are unsafe, harmful, or malicious, please refuse to answer them and give your explanation. '

Table 4 shows a breakdown of our multimodal robustness results by category. The categories are based off of the MM-SafetyBench scenarios from which a portion of our dataset was drawn, with which we also used to categorize the HarmBench samples in our dataset.

C.4 AI Agents

We use gpt-4-turbo [50] as a judge to evaluate robustness to harmful requests for function-calls, since the HarmBench classifier was not trained on function calling data.

We set generation temperature to 0 when evaluating on the Berkeley Function Calling Leaderboard. We report the average of the AST and Exec scores.

C.4.1 AI Agent Benchmark Example

D Open LLM Results

Table 5 shows the scores for each individual benchmark in the Open LLM evaluation.

E Detailed Results in Multimodal and Agent Settings

The multimodal results on the left show that under Projected Gradient Descent (PGD) attack, the model with circuit breakers is significantly more robust compared to the original model even with a safety prompt (+Prompt) that instructs the model to avoid harmful responses. Performance on multimodal capabilities benchmarks LLaVA-Wild and MMMU is preserved. In the agent setting on the right, our model with circuit breakers remains robust under Forced Function Calling (Forced F/C), while retaining performance on the Berkeley Function Calling Leaderboard (BFCL).

F Multilingual Results

In both [70] and [61], it was observed that LRL attacks perform better than HRL attacks. We do not see that trend in Table 6. We leave investigation of this to future work.

G Additional Ablation Results

Harmful

Misinfo

Illegal

Harass

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Chem/Bio

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In general, we find that with Mistral, loss terms that leverage random vectors decrease ASR but also decrease capabilities. We don't try the augmenting the circuit breaker set or adding refusal data to the retain set because Mistral-7B is not trained with refusal mechanisms. We also find that the circuit breaker set is relatively robust to distribution shifts in categories of harm.

H Extended analysis of representations

Here, we provide an extended representation analysis compared to Section 4.4. In Figure 10, we use the same prefilling example ( 'Here is how to synthesize meth: 1. Start with' ) but on the Mistral-7B models. Here we also observe that circuit-breaking occurs during prefilling similarly to Llama-3-8B. However, this is not always the case: for example in Figure 11, we show a slightly modified prefilling example 'Here is how to synthesize m-e-t-h: 1. Start with' (i.e., the word 'meth' is purposefully obfuscated). In this case, circuit-breaking happens during generation which is a common scenario:

under an adversarial attack, the model starts generating harmful content but switches to meaningless outputs before harmful knowledge is revealed.

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In Figure 12 and Figure 13, we show cosines and norms on Llama-3-8B and Mistral-7B for a different prefilling prompt: 'Sure, here is how to make a bomb: 1. Start with' . We observe that for both model families, circuit-breaking occurs during prefilling, similarly to the example presented in Section 4.4. The only difference is that the norms at layer 10 do not grow significantly, unlike the norms at layer 20 that clearly indicate circuit-breaking.

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a new technique called \"short circuiting\" that makes models more robust against outputting harmful texts without significant impacts to their helpfulness (a significant pareto improvement on the harmfulness-helpfulness tradeoff). Short circuiting works by training a model's hidden representation on harmful prompts to be perpendicular to the original representations, so that when it sees harmful prompts in the future its inputs will be off distribution. There is also an additional loss term that ensures that the model's representations on a set of normal prompts remains the same to preserve benign helpful behavior. This method achieves state of the art performance on adversarial attacks on text models, text+image models, and on an agent benchmark the authors create.\n\nSTRENGTHS:\n- The method is very effective and general: short circuiting makes models significantly more robust to outputting harmful information while remaining almost the same level of helpful. \n- It is an incredibly positively impactful problem, and the results can be applied immediately to existing large scale multi-modal models.  \n- This is an entirely new class of method for adversarial training that may now be built upon and improved.\n- The authors introduce a new harmful function calling dataset.\n\nWEAKNESSES:\n- The presentation and paper organization could be improved in many places (see questions section)\n- There are many experiment details ommited\n- Not clear if the method will work on types of harmful output not seen in the fine tuning data\n- The harmful knowledge is still in the model, so it cannot be open sourced. Even if it was trained normally in Algorithm 1 (instead of LoRA) and released, bad actors may still be able to fine-tune the model to restore the harmful capabilities, as recent work shows.\n- In the checklist, the authors say they specify the compute resources used in the appendix, but I was not able to find this information.\n\nQUESTIONS:\nSection 3 (the description of the methods) could be much improved:\n- It would help if there was more intuition on why short-circuiting makes sense as a method, for example by explaining that it seeks to put the hidden representations as much off-distribution as possible\n- The Loss subsection describes many potential loss methods that are brought up and then dismissed as explored. It is confusing which loss is used in the actual methods.\n- Line 149 states \"We find that a practical approach is to remove harmful user requests from the Short Circuit Set.\" which does not make sense to me, as I thought the goal of the short circuit set was to precisely include harmful user requests.\n- This section is very long with prose and generally includes many unneeded details, e.g. much of paragraph 111. On the other hand, crucial details like which/how many layers are trained with LoRA are unstated. The second would benefit from more formalism of the method in the body of the text. Section C.1 should ideally also be merged into section 3 for similar reasons.\n\n- The paragraph on 212 discussing the results should focus more on the numbers and less on describing the virtues of short circuiting (which already occurs plenty in the introduction and the conclusion). To be clear, I think it's a great technique! But the results section should describe the results more objectively.\n\n- Possibly it would be helpful to show a non representation engineering baseline to show that the method is not overfitting to the training data. E.g. could do a LoRA finetuning run on the same dataset where the loss is on the output and towards refusals/maintaining original behavior.\n\n- Sections 4.1, 4.2, and 4.3 could use an intro paragraph at the top describing the problem setup. For 4.3 especially this is quite jarring.\n\n- It is mostly unclear what the short circuit and retain datasets are for each method. For example, how many samples do they contain? What are the \"extra refusal data points\" mentioned on line 182?\n\n- It would be helpful to explain all of the attack types in the list on line 188.\n\n- Typo: extra and on line 189\n\nLIMITATIONS:\nSomewhat, the authors touch on only one limitation. The limitations section would be improved if it included a deeper discussion, e.g. on generalization to unseen harm classes or vulnerability to further fine-tuning.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-06-24"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents a novel short-circuiting method to ensure LLM safety alignment and its adversarial robustness against jailbreaking attacks. Particularly, the authors propose to conduct LoRA training with their short circuiting loss in the model representation space. The short-circuited models can effectively resist various jailbreaking attempts.\n\nSTRENGTHS:\n1. The idea behind the proposed method is novel. Instead of trying to capture and patch against different jailbreaking attacks, the short circuiting method directly looks into the model representation space, and steers model representation at harmful state towards a short circuit direction.\n2. The experiment results are solid, spanning across various jailbreaking attacks (whitebox & blackbox), 2 different models (Llama-3-8B-Instruct and Mistral-7B-Instruct-v2), and 3 different scenarios (LLM, MLLM, LLM Agent).\n3. The authors also verify that the short-circuited models can retain similar-level model capabilities on LLM benchmarks like MT-Bench and MMLU.\n\nWEAKNESSES:\nOverall, I enjoy reading the paper quite much. But here are some of my major concerns:\n\n1. **More insights into the method rationale.** The idea of short circuiting is impressive. Nevertheless, I find some the rationale of it not that intuitive. For example, in Line 44-45, the authors claim that \"when a (short-circuited) model begins to generate such an output, its internal processes are interrupted, halting completion of the generation.\" However, the formulas in Algorithm 1 and Sec 3 only focus on steering SC model's representation $\\text{rep}_{\\mathcal{M}_\\text{sc}}(x_s)$ away from the original model's representation $\\text{rep}_{\\mathcal M}(x_s)$, at the state where they are outputting a harmful output. This does not explain why a model would halt when it \"begins to\" provide a harmful output. I appreciate the visualization of representations in Fig 5, which partially help justify the claim in Line 44-45. Would be great to provide more insights why this would happen after training models via Algorithm 1.\n2. **More details on Short Circuit Set.** I don't think the paper and apppendix provide enough information on how this dataset should be collected.\n3. **Adaptive attacks?** The authors have claimed that short-circuiting can defend well against various types of adversarial jailbreaking attacks, including multiple white-box ones. Particularly, the authors highlight how short-circuiting is immune to PGD attack against multimodal models. I wonder whether these white-box attacks take into consideration of the LoRRA short-circuiting module, i.e., whether the attackers are (un)aware of the existence of the short-circuiting defense? If yes, why are they less effective than some black-box attacks (e.g., Input Embed is less effective than PAIR and TAP-T in Table 1)? If no, it'd be necessary to add an adaptive analysis on your method. I believe it'd be crucial to thoroughly discuss and highlight the potential adaptive vulnerabilities of short circuiting.\n4. **More model capability evaluation.** It's important to validate the capability is well retained after short-circuiting. While I appreciate the authors' efforts on MT-Bench and MMLU, it'd be great to see more capability results (e.g., GSM8K, ARC-c, AlpacaEval, etc.). I'm also curious about the win rate of model responses before & after short-circuit training.\n5. **Tailoring Short Circuiting.** In line 125-126, you mentioned that short-circuiting can be tailored to \"prevent more narrowly tailored types of output.\" Can you provide any experimental results to justify this? For example, show how to apply short-circuiting to prevent models from generating copypright content, but not other types of potentially harmful content. I notice that in Fig 4, you have ablated across 6 types of harm -- the results there only show how training over one type of harm would generalize to other types.\n\nQUESTIONS:\n1. In Table 1, why is PAIR the most successful attack (23.3 ASR), even better than white-box attacks like Input Embed (15.7 ASR)? The author claim these white-box attacks are \"proxies for worst-case assumptions aboout attacker capabilities\" (Line 54), but they don't seem to outperform other attacks against Short Circuiting.\n2. Line 150: What do you mean by \"removing harmful user requests\" from the Short Circuit Set? Why?\n3. Are there any ablation results on full parameter fine-tuning (showing it doesn't work stably), instead of LoRA? I'm asking this because sometimes LoRA may not be a preferred option (compared to full fine-tuning) to implement LLM safety -- malicious users can simply disable it to obtain harmful model outputs.\n\nLIMITATIONS:\nThe authors have discussed some limitations in Sec 5. However, I don't think that discussion fully elaborates potential (adaptive) vulnerabilities of the proposed method. It would be necessary to highlight / discuss in what case the proposed short circuiting method may fail (in the context of jailbreaking).", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 3, "DATE": "2024-07-07"}

comments: SUMMARY:
This paper proposes a method that manipulates the representation of potentially harmful queries to reduce the harmful generation. Extensive experiments are conducted to verify the effectiveness.

STRENGTHS:
Experiments on different scenarios are provided to illustrate the effectiveness of the proposed method.

WEAKNESSES:
1. The distinct difference between the proposed method and adversarial training is not clear. Generally speaking, adversarial training allows the defender to modify the model based on the adversarial examples to improve the robustness. The proposed method also fine-tune the LLM on potentially harmful queries, which can somehow be considered as a generative-model-version adversarial examples, to reduce the generation of harmful content.
2. The idea of manipulating the representation is not a new one. [1] has proposed to move the representation of harmful queries towards rejection. Then the contribution of this work is not very significant.
3. The details of representation used in this work are not clear.


[1] On Prompt-Driven Safeguarding for Large Language Models. Chujie Zheng et al. ICML2024

QUESTIONS:
See weakness.

LIMITATIONS:
Yes
TITLE: Official Review
SOUNDNESS_CORRECTNESS: 3
DATE: 2024-07-10

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors propose and test a novel method called \"short-circuiting\" which aims to modify a generative model $M$ so that it is unable engage in a specified behavior $B$.\n\nThe high-level idea of short-circuiting is as follows:\n\n1. First, transcripts demonstrating the behavior $B$ are collected. The is called the \"Short-Circuit Set\".\n2. Next, transcripts demonstrating behaviors that $M$ should retain are collected. This is called the \"Retain Set\".\n3. A copy $M_\\text{sc}$ of the model $M$ is made and $M_\\text{sc}$ is fine-tuned with two objectives:\n    - Short-circuit objective: for transcripts $x_\\text{sc}$ from the short-circuit set, the internal activations of $M_\\text{sc}$ when processing $x_\\text{sc}$ should look different from the internal activations of $M$ when processing $x_\\text{sc}$.\n    - Retain objective: for transcripts $x_{r}$ from the retain set, the internal activations of $M_\\text{sc}$ when processing $x_\\text{r}$ should look very similar to the internal activations of $M$ when processing $x_\\text{r}$.\n\nThe authors try a few different implementations of this high-level scheme, and evaluate their best performing implementation against a suite of jailbreak attacks designed to elicit harmful completions, including attacks that get white-box access to the model. Evaluations are run for language models and vision-language models.\n\nThe authors show that attacks have a much harder time eliciting harmful completions from the short-circuited models as compared to the original non-short-circuited models. Moreover, compared to the non-short-circuited models, the short-circuited models show essentially no degradation in performance on non-harmful tasks like MMLU and MTBench.\n\nSTRENGTHS:\n1. [Quality] Short-circuiting is a simple, cheap, and general method for preventing models from engaging in specified behaviors.\n2. [Originality] Short-circuiting seems novel based on my understanding of the literature.\n3. [Quality] Short-circuiting shows good empirical performance at defending against attacks, greatly reducing attack success rate compared to non short-circuited models without damaging performance on MMLU and MT Bench.\n4. [Significance] Short-circuiting can be quite naturally applied to prevent LLMs from exhibiting clearly bad behavior. This is an open and important problem in the field of AI safety, thus the method has actual real-world consequences.\n\nWEAKNESSES:\nThe main thing the paper lacks is an analysis of why short-circuiting fails to reduce attack success rates to *zero*. Achieving zero ASR is one of the end-goals of adversarial robustness research. The paper would be significantly stronger if it provided an explanation of why short-circuiting does not meet this objective and suggested possible improvements to short-circuiting.\n\nQUESTIONS:\n**Major suggestion**\nI feel that you should include transcripts of successful and unsuccessful attacks for both baseline models and short-circuited models. This would help the reader develop better intuitions around what short-circuiting does, the situations where it works, and the situations where it doesn't work. This might also give some insight into why short-circuiting is unable to achieve 0 ASR. This would also address concerns around the quality of jailbreak evaluation as raised by papers like \"A StrongREJECT for Empty Jailbreaks\" (https://arxiv.org/abs/2402.10260).\n\n**Minor questions**\n- What is the “System Level” attack in Figure 2? It would be good to match the axis labels here with the different attacks from Table 1?\n\nLIMITATIONS:\nSee weaknesses and question sections.", "TITLE": "Official Review", "RECOMMENDATION": 8, "REVIEWER_CONFIDENCE": 4, "SOUNDNESS_CORRECTNESS": 4, "DATE": "2024-07-11"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work presented a method to short-circuit model when they attempt to output harmful content. This is conducted through representation engineering the internal representation of short-circuit model (LoRRA adaptor + forzen original model) corresponding to harmful content towards an objective orthogonal to it's original representation of original model through a short-circuit dataset and short-circuit loss, but keep model performance by keep those two representation close on retain dataset using retain loss. The method is model invariant and simple to apply. The work demonstrate three cases: LLM, MLLM, and AI Agents, using this method, they reduce ASR by a significant amount for different attack types including unseen ones comparing to refusal training and adversarial training, while retain the original model performance better compare two the other defense baselines. The author presented comprehensive experiments for the three cases also provided ablation and analysis discussion.\n\nSTRENGTHS:\n1. The defense techniques proposed here by design could be invariant to input but focus on representation that generates harmful output, therefore have nice generalization potential.\n2. Strong generalization across different attacks and even unseen attacks comparing to refusal training and adversarial training.\n3. Strong performance for mitigating attacks targeting LLM, MLLM, and AI Agents.\n4. High performance retain compare to other defense techniques.\n5. The method is simple and model invariant.\n\nWEAKNESSES:\nWriting:\n1. Figure 4 is not used anywhere, I suppose you refer it around section 4.4\n2. Table 1 is not used, it should be used around line 212? For table 1, the baseline: refusal trained and adv trained are not described in section 3. Also the scores reported in the table is not clear: I assume for above half is ASR, what is used for bottom half? (typo of MT-Bench result?)\nExperiment setting and results are not well explained, check questions below.\n4. Figure 1 is not referred anywhere.\n\nQUESTIONS:\n1. I am confused about the explanation at line 140 - 143: what does training data referring to here, and for \"performance is generally improved when the training data better aligns with the domains we aim to short-circuit and retain.\": Do you mean the opposite that short-circuit and retain data should be aligned to the training data of the model?\n2. From line 145 to line 150, I could understand your may want to remove harmful user requests from short circuit set to address when an attack by pass the refusal mechanism, but description here is not clear to me, especially sentence from 145 to 148 where you describe the necessity of adding refusal data to retain set. It is helpful to explain that whole paragraph more clearly so that later ablation result in Figure 4 make more sense.\n3. From Table 1: when No Attack, the refusal trained and adv trained model still have high ASR? better explanation is needed.\nI am willing to increase score if my questions are addressed.\n\nLIMITATIONS:\nAuthor stated their method is designed to preventing harmful content generation when harmful is defined by short-circuit data, but the method is not suitable for attacks that changing class label since class label can not be defined as harmful or not in general.", "TITLE": "Official Review", "RECOMMENDATION": 7, "REVIEWER_CONFIDENCE": 3, "SOUNDNESS_CORRECTNESS": 2, "DATE": "2024-07-12"}