GenAI4ELab/papercli-papers huggingface dataset:
Efficient, Secure, Differentially Private Deep Learning in the Two-Server Model
Authors: Jun Feng; Hong Sun; Pengfei Zhang; Bocheng Ren; Shunli Zhang
Venue: AAAI 2026 (main)

Abstract: Existing solutions on differentially private deep learning (DPDL) either require the assumption of a trusted data server (centralized DPDL) or suffer from poor utility (local DPDL); and hence their adoptions are hampered in real-world scenarios.We present CRYPTDP, a crypto-assisted differentially private deep learning approach in the two-server model. CRYPTDP employs two non-colluding servers to collaboratively and efficiently train differentially private deep learning over the secret shares of data owners' private data while protecting the confidentiality of the data from untrusted servers. CRYPTDP is the first approach with the best of both local DPDL and centralized DPDL models, which does not resort to trusted server like local DPDL and has the utility like centralized DPDL. In particular, we also make innovations for addressing the major challenges like poor performance and security that beset CRYPTDP: We introduce a new secure computation and differential privacy friendly activation function; we propose a novel garbled-circuits-free most significant bit extraction protocol, and using the protocol we propose an efficient and secure garbled-circuits-free protocol for activation function over secret shares. Exhaustive experiments show that CRYPTDP delivers significantly better performance than the state-of-the-art local DPDL, yields higher accuracy than the state-of-the-art centralized DPDL, and can achieve two orders of magnitude faster runtime than the state-of-the-art approach.
Runtime Safety and Reach-avoid Prediction of Stochastic Systems via Observation-aware Barrier Functions
Authors: Shenghua Feng; Jie An; Fanjiang Xu
Venue: AAAI 2026 (main)

Abstract: Stochastic dynamical systems have emerged as fundamental models across numerous application domains, providing powerful mathematical representations for capturing uncertain system behavior. In this paper, we address the problem of runtime safety and reach-avoid probability prediction for discrete-time stochastic systems with online observations, i.e., estimating the probability that the system satisfies a given safety or reach-avoid specification. Unlike traditional approaches that rely solely on offline models, we propose a framework that incorporates real-time observations to dynamically refine probability estimates for safety and reach-avoid events. By introducing observation-aware barrier functions, our method adaptively updates probability bounds as new observations are collected, combining efficient offline computation with online backward iteration. This approach enables rigorous and responsive prediction of safety and reach-avoid probabilities under uncertainty. In addition to the theoretical guarantees, experimental results on benchmark systems demonstrate the practical effectiveness of the proposed method.
RSA-CR: Resisting Shilling Attacks in Citation Recommendation via Dumbbell Inductive Learning
Authors: Xiyue Gao; Yukai Liu; Zhuoqi Ma; Xiaotian Qiao; Hui Li; Cai Xu; Kunhua Zhang; Jiangtao Cui
Venue: AAAI 2026 (main)

Abstract: Citation recommendation aims to provide researchers with the most relevant references for their manuscripts, helping them swiftly discover pertinent studies and bolster the reliability of their arguments. However, some individuals manipulate these recommendation systems by injecting false information, such as deliberately inflating the citation count of their own papers, to obtain favorable recommendations and ratings. This form of attack, commonly termed “shilling attack”, is not only highly concealed but also has an unimaginable impact on all scientific research. To address this problem, we theoretically reveal the impact of shilling attacks on citation recommendation and propose three feasible resistance strategies: historical collaborations, significant citations and content constraints. Based on these insights, we introduce RSA-CR, a robust and hybrid citation recommendation algorithm resistant to shilling attacks. The algorithm constructs a two-layer academic graph and uses random and content generation strategies to initialize author and paper embeddings. Confidence-guided inductive aggregations based on collaboration and citation relationships are then performed at the author and paper sides, where author aggregation results directly influences the paper aggregation strength. Finally, recommendations are made by measuring the distances between the fused paper embeddings. The entire learning process resembles a dumbbell, hence termed “dumbbell inductive learning”. Experiments on four academic datasets demonstrate that our method outperforms baselines in both effectiveness and robustness.
6DAttack: Backdoor Attacks in the 6DoF Pose Estimation
Authors: Jihui Guo; Zongmin Zhang; Zhen Sun; Yuhao Yang; Jinlin Wu; Fu Zhang; Xinlei He
Venue: AAAI 2026 (main)

Abstract: Recent advances in deep learning have enabled highly accurate six-degree-of-freedom (6DoF) object pose estimation, leading to its widespread use in real-world applications such as robotics, augmented reality, virtual reality, and autonomous systems. However, backdoor attacks pose a major security risk to deep learning models. By injecting malicious triggers into training data, an attacker can cause a model to perform normally on benign inputs but behave incorrectly under specific conditions. While most research on backdoor attacks has focused on 2D vision tasks, their impact on 6DoF pose estimation remains largely unexplored. Furthermore, unlike traditional backdoors that only change the object class, backdoors against 6DoF pose estimation must additionally control continuous pose parameters, such as translation and rotation, making existing 2D backdoor attack methods not directly applicable to this setting.

To address this gap, we propose a novel backdoor attack framework (6DAttack) that exposes vulnerabilities in 6DoF pose estimation. 6DAttack uses synthetic and real 3D objects of varying shapes as triggers and assigns target poses to induce controlled erroneous pose outputs while maintaining normal behavior on clean inputs. We evaluated this attack on multiple models (including PVNet, DenseFusion, and PoseDiffusion) and datasets (including LINEMOD, YCB-Video, and CO3D). Experimental results demonstrate that 6DAttack achieves extremely high attack success rates (ASRs) without compromising performance on legitimate tasks. Across various models and objects, the backdoored models achieve up to 100% ADD accuracy on clean data, while also achieving 100% ASR under trigger conditions. The accuracy of controlled erroneous pose output is also extremely high, with triggered samples achieving 97.70% ADD-P. These results demonstrate that the backdoor can be reliably implanted and activated, achieving a high ASR under trigger conditions while maintaining a negligible impact on benign data. Furthermore, we evaluate a representative defense and show that it remains ineffective under 6DAttack. Overall, our findings reveal a potentially serious and previously underexplored threat to modern 6DoF pose estimation models.
The Silent Amplifier: In-Context Examples Fuel Bias in Large Language Models
Authors: Xinwei Guo; Jiashi Gao; Junlei Zhou; Jiaxin Zhang; Quanying Liu; Haiyan Wu; Xin Yao; Xuetao Wei
Venue: AAAI 2026 (main)

Abstract: In-context learning (ICL) has proven to be adept at adapting large language models (LLMs) to downstream tasks without parameter updates, based on a few demonstration examples. Prior work has found that the ICL performance is susceptible to the selection of examples in prompt and made efforts to stabilize it. However, existing example selection studies ignore the ethical risks behind the examples selected, such as gender and race bias. In this work, we conduct extensive experiments and discover that (1) example selection with high accuracy does not mean low bias;  (2) example selection for ICL may amplify the biases of LLMs; (3) example selection contributes to spurious correlations of LLMs. Based on the above observations, we propose the Remind with Bias-aware Embedding (ReBE), which removes the spurious correlations through contrastive learning and obtains bias-aware embedding for LLMs based on prompt tuning. Finally, we demonstrate that ReBE effectively mitigates biases of LLMs without significantly compromising accuracy and is highly compatible with existing example selection methods.
Beyond World Models: Rethinking Understanding in AI Models
Authors: Tarun Gupta; Danish Pruthi
Venue: AAAI 2026 (main)

Abstract: World models have garnered substantial interest in the AI community. These are internal representations that simulate aspects of the external world, track entities and states, capture causal relationships, and enable prediction of consequences. This contrasts with representations based solely on statistical correlations. A key motivation behind this research direction is that humans possess such mental world models, and finding evidence of similar representations in AI models might indicate that these models "understand" the world in a human-like way. In this paper, we use case studies from the philosophy of science literature to critically examine whether the world model framework adequately characterizes human-level understanding. We focus on specific philosophical analyses where the distinction between world model capabilities and human understanding is most pronounced. While these represent particular views of understanding rather than universal definitions, they help us explore the limits of world models.
Activation Manipulation Attack: Penetrating and Harmful Jailbreak Attack Against Large Vision-Language Models
Authors: Haojie Hao; Jiakai Wang; Aishan Liu; Yuqing Ma; Haotong Qin; Yuanfang Guo; Xianglong Liu
Venue: AAAI 2026 (main)

Abstract: Recently, Large Vision-Language Models (LVLMs) have been demonstrated to be vulnerable to jailbreak attacks, highlighting the urgent need for further research to comprehensively identify and mitigate these threats.
Unfortunately, existing jailbreak studies primarily focus on coarse-grained input manipulation to elicit specific responses, overlooking the exploitation of internal representations, i.e., intermediate activations, which constrains their ability to penetrate alignment safeguards and generate harmful responses.
To tackle this issue, we propose the Activation Manipulation (ActMan) Attack framework, which performs fine-grained activation manipulations inspired by the perception and cognition stages of human decision-making, enhancing both the penetration capability and harmfulness of attacks.
To improve penetration capability, we introduce a Deceptive Visual Camouflage module inspired by the masking effect in human perception. This module uses a benign activation-guided attention redirection strategy to conceal abnormal activation patterns, thereby suppressing LVLM's defense detection during early-stage decoding.
To enhance harmfulness, we design a Malicious Semantic Induction module drawing from the framing effect in human cognition, which reconstructs jailbreak instructions using malicious activation guidance to change LVLM’s risk assessment during late-stage decoding, thereby amplifying the harmfulness of model responses.
Extensive experiments on six mainstream LVLMs demonstrate that our method remarkably outperforms state-of-the-art baselines, achieving an average relative ASR improvement of 12.06%.
FILTER: A Framework for Defending Against Backdoor Attacks in Vertical Federated Learning
Authors: Zhanyi Hu; Cen Chen; Yanhao Wang
Venue: AAAI 2026 (main)

Abstract: Vertical Federated Learning (VFL) is a distributed machine learning paradigm in which participants train models with vertically partitioned data. Many previous studies have identified backdoor vulnerabilities in VFL systems. However, limited effort has been devoted to developing defenses against such attacks. Unlike centralized machine learning or horizontal FL, VFL poses new challenges for defending against backdoor attacks, particularly because the central server lacks control over the entire model. In this paper, we first explore defenses against backdoor attacks in VFL when the attacker possesses sufficient knowledge of the label information. Specifically, we propose FILTER, a framework for defending against backdoor attacks in VFL to ensure the integrity of VFL systems during training in the presence of malicious participants. To address backdoor risks in VFL, it incorporates two novel filters: an embedding-based filter and a loss-based filter, which effectively identify and remove poisoned samples in later stages of training. Through extensive experiments on five benchmark datasets against four state-of-the-art backdoor attacks, we demonstrate that FILTER significantly reduces the success rate of attacks while maintaining accuracy on clean data close to that of the models trained without such defenses.
Parameterized Abstract Interpretation for Transformer Verification
Authors: Pei Huang; Dennis Wei; Omri Isac; Haoze Wu; Min Wu; Clark Barrett
Venue: AAAI 2026 (main)

Abstract: Transformers based on the self-attention mechanism have become foundational models across a wide range of domains, thereby creating an urgent need for effective formal verification techniques to better understand their behavior and ensure safety guarantees. In this paper, we propose two parameterized linear abstract domains for the inner products in the self-attention module, aiming to improve verification precision. The first one constructs symbolic quadratic upper and lower bounds for the product of two scalars, and then derives parameterized affine bounds using tangents. The other one constructs parameterized bounds by interpolating affine bounds proposed in prior work. We evaluate these two parameterization methods and demonstrate that both of them outperform the state-of-the-art approach which is regarded as optimal with respect to a certain mean gap. Experimental results show that, in the context of robustness verification, our approach is able to verify many instances that cannot be verified by existing methods. In the interval analysis, our method achieves tighter results compared to the SOTA, with the strength becoming more pronounced as the network depth increases.
Any2Critical: Safety-Critical Scenario Generation from Arbitrary Real-World Driving Contexts
Authors: Yao Huang; Yubo Chen; Ruochen Zhang; Yitong Sun; Shouwei Ruan; Zhenyu Wu; Yinpeng Dong; Xingxing Wei
Venue: AAAI 2026 (main)

Abstract: Autonomous driving systems have achieved remarkable capabilities in real-world deployment, yet ensuring safety under corner cases remains a significant challenge due to the scarcity and constrained diversity of safety-critical scenarios. Existing generation methods may either lead to irrational vehicle behaviors or be limited by fixed collision patterns, while both heavily rely on existing map datasets, restricting the diversity. To address these fundamental limitations, we introduce Any2Critical, the first framework that can encode arbitrary real-world scenarios and generate contextually relevant safety-critical scenarios with realistic driving behaviors. Specifically, Any2Critical addresses two key challenges: (1) developing comprehensive, diverse map data by successfully leveraging everyday traffic situations as the most abundant source of real-world driving contexts, and (2) proposing an RAG-based Safety-Critical Scenario Generation Strategy based on our curated NHTSA-5K database for achieving an optimal balance between scenario diversity and behavioral rationality. Through comprehensive evaluation, we demonstrate that Any2Critical consistently achieves collision rates with an average of 89.69% across diverse scenarios and autonomous driving systems, significantly outperforming current state-of-the-art generation methods.
Private Frequency Estimation via Residue Number Systems
Authors: Héber Hwang Arcolezi
Venue: AAAI 2026 (main)

Abstract: We present Modular Subset Selection (MSS), a new algorithm for locally differentially private (LDP) frequency estimation. Given a universe of size k and n users, our ε-LDP mechanism encodes each input via a Residue Number System (RNS) over ℓ pairwise-coprime moduli m0, ..., m_{ℓ−1}, and reports a randomly chosen index j ∊ [ℓ] along with the perturbed residue using the statistically optimal Subset Selection (SS) mechanism. This design reduces the user communication cost from Θ(ω log₂(k/ω)) bits required by standard SS (with ω ≈ k/(e^ε+1)) down to ⌈ log₂ ℓ ⌉ + ⌈ log₂ m_j ⌉ bits, where m_j < k. Server-side decoding runs in Θ(n + r k ℓ) time, where r is the number of LSMR iterations. In practice, with well-conditioned moduli (i.e., constant r and ℓ = Θ(log k)), this becomes Θ(n + k log k). We prove that MSS achieves worst-case MSE within a constant factor of state-of-the-art protocols such as SS and Projective Geometry Response (PGR), while avoiding the algebraic prerequisites and dynamic-programming decoder required by PGR. Empirically, MSS matches the estimation accuracy of SS, PGR, and RAPPOR across realistic (k, ε) settings, while offering faster decoding than PGR and shorter user messages than SS. Lastly, by sampling from multiple moduli and reporting only a single perturbed residue, MSS achieves the lowest reconstruction-attack success rate among all evaluated LDP protocols.
Efficient LLM-Jailbreaking via Multimodal-LLM Jailbreak
Authors: Haoxuan Ji; Zheng Lin; Zhenxing Niu; Xinbo Gao; Gang Hua
Venue: AAAI 2026 (main)

Abstract: This paper focuses on jailbreaking attacks against large language models (LLMs), eliciting them to generate objectionable content in response to harmful user queries. Unlike previous LLM-jailbreak methods that directly orient to LLMs, our approach begins by constructing a multimodal large language model (MLLM) built upon the target LLM. Subsequently, we perform an efficient MLLM jailbreak and obtain a jailbreaking embedding. Finally, we convert the embedding into a textual jailbreaking suffix to carry out the jailbreak of target LLM. Compared to the direct LLM-jailbreak methods, our indirect jailbreaking approach is more efficient, as MLLMs are more vulnerable to jailbreak than pure LLMs. Additionally, to improve the attack success rate of jailbreak, we propose an image-text semantic matching scheme to identify a suitable initial input. Extensive experiments demonstrate that our approach surpasses current state-of-the-art jailbreak methods in terms of both efficiency and effectiveness. Moreover, our approach exhibits superior cross-class generalization abilities.
MedOmni-45°: A Safety–Performance Benchmark for Reasoning-Oriented LLMs in Medicine
Authors: Kaiyuan Ji; Yijin Guo; Zicheng Zhang; Xiangyang Zhu; Yuan Tian; Ning Liu
Venue: AAAI 2026 (main)

Abstract: With the rapid integration of large language models (LLMs) into medical decision-support aids, ensuring reliability in reasoning steps—not just final answers—is increasingly critical. Two key safety dimensions are Chain-of-Thought (CoT) faithfulness, which assesses alignment of the model’s reasoning process with both its response and medical facts, and sycophancy, an emergent misalignment where models follow misleading cues instead of factual correctness. Yet existing benchmarks tend to prioritize performance evaluation, frequently collapsing nuanced safety vulnerabilities into a single accuracy score. To fill this gap, we introduce MedOmni-45°, a benchmark and evaluation workflow explicitly designed to quantify the safety–performance trade-off in LLMs under manipulative hint conditions. The benchmark contains 1,804 reasoning-focused medical questions across six clinical specialties and three task types, including 500 publicly comparable items from MedMCQA. Each question is systematically augmented with seven manipulative hint types, each embedding two distinct misleading cue variants, along with a No-Hint baseline, resulting in approximately 27,000 unique inputs. These inputs are then evaluated across seven LLMs spanning open- and closed-source, general-purpose and medical-specific, and base versus reasoning-enhanced variants, amounting to over 189K total inference instances. Three orthogonal metrics (Accuracy, CoT-Faithfulness, Anti-Sycophancy) are combined into a composite score visualized via a 45° safety–performance plot. Results reveal a universal trade-off, with no model surpassing the ideal diagonal. Open-source QwQ-32B approaches closest at 43.81°, demonstrating notable safety while not surpassing others in performance. MedOmni-45° thus highlights critical vulnerabilities of LLMs in reasoning oriented medical tasks, offering a robust benchmark for future alignment research.
Higher-Order Responsibility
Authors: Junli Jiang; Pavel Naumov
Venue: AAAI 2026 (main)

Abstract: In ethics, individual responsibility is often defined through Frankfurt's principle of alternative possibilities. This definition is not adequate in a group decision-making setting because it often results in the lack of a responsible party or "responsibility gap". One of the existing approaches to address this problem is to consider group responsibility. Another, recently proposed, approach is  "higher-order" responsibility. The paper considers the problem of determining whether higher-order responsibility up to a given degree is sufficient to close the responsibility gap and analyses the computational complexity of this problem.
SceneJailEval: A Scenario-Adaptive Multi-Dimensional Framework for Jailbreak Evaluation
Authors: Lai Jiang; Yuekang Li; Xiaohan Zhang; Youtao Ding; Li Pan
Venue: AAAI 2026 (main)

Abstract: Accurate jailbreak evaluation is critical for LLM red team testing and jailbreak research. Mainstream methods rely on binary classification (string matching, toxic text classifiers, and LLM-based methods), outputting only "yes/no" labels without quantifying harm severity. Emerged multi-dimensional frameworks (e.g., Security Violation, Relative Truthfulness and Informativeness) use unified evaluation standards across scenarios, leading to scenario-specific mismatches (e.g., "Relative Truthfulness" is irrelevant to "hate speech"), undermining evaluation accuracy. To address these, we propose SceneJailEval, with key contributions:
(1) A pioneering scenario-adaptive multi-dimensional framework for jailbreak evaluation, overcoming the critical "one-size-fits-all" limitation of existing multi-dimensional methods, and boasting robust extensibility to seamlessly adapt to customized or emerging scenarios.  
(2) A novel 14-scenario dataset featuring rich jailbreak variants and regional cases, addressing the long-standing gap in high-quality, comprehensive benchmarks for scenario-adaptive evaluation.  
(3) SceneJailEval delivers state-of-the-art performance with an F1 score of 0.917 on our full-scenario dataset (+6% over SOTA) and 0.995 on JBB (+3% over SOTA), breaking through the accuracy bottleneck of existing evaluation methods in heterogeneous scenarios and solidifying its superiority.