Language Model Security Database

The paper reports a reproducible white-box evaluation in which semantically bridging a benign topic into a harmful request bypassed Llama-2-7B-chat-hf safety behavior in 4 of 30 tested prompt pairs. Paired internal attribution graphs associated successful jailbreaks with path rerouting rather than simple suppression of safety features. This is a paper-reported result, not independently verified here. Defensive reproduction should use the paper’s supplied dataset and code in an isolated…

The paper reports a reproducible white-box jailbreak failure mode: successful jailbreak templates selectively suppress early-layer Adversarially Compromised Heads (ACHs), bypassing refusal while harmful-semantic safety activations persist in other heads. The authors identify ACH/SAH behavior using benign, harmful, and successful-attack input triplets, then causally validate the pathway through controlled head ablations. Safe defensive reproduction should use the paper’s released evaluation…

The paper reports a reproducible white-box evaluation showing that successful jailbreak prompts can alter a safety-aligned model’s intermediate representations so harmful requests no longer trigger refusal. Its LOCA method identifies small, token-specific residual-stream changes that restore refusal on individual successful jailbreaks, providing causal evidence that jailbreak success can depend on suppressing harmfulness/refusal concepts or strengthening seemingly harmless continuation…

Vision-Language Models (VLMs) are vulnerable to pixel-level adversarial image perturbations. An attacker can inject $\ell_p$-bounded, human-imperceptible noise into an input image to manipulate the model's multi-modal embedding space. This reliably causes the VLM to generate incorrect textual responses, hallucinate non-existent objects, or misclassify subjects, effectively decoupling the model's reasoning from the actual visual evidence. The vulnerability is exploitable via both white-box…

Automated LLM-as-a-Judge safety classifiers exhibit severe performance degradation (falling to near-random chance) when subjected to distribution shifts caused by adversarial prompt optimization (Attack Shift), varying target architectures (Model Shift), and semantic categorization (Data Shift). Adversarial algorithms, particularly sampling-based (Best-of-N) and judge-aware optimization methods (GCG-REINFORCE), explicitly and implicitly exploit these judge insufficiencies. Instead of eliciting…

Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…

LLaVA-v1.5-7B, when deployed as a vision-language autonomous agent, is highly vulnerable to adversarial image perturbations. An attacker can inject imperceptibly modified images into a web environment (such as an e-commerce storefront). When the VLM agent captures a screenshot containing the perturbed image, the visual noise forces the model to misclassify the scene and output incorrect, structured JSON actions. This allows an attacker to hijack the agent's task execution, bypassing the user's…

Video-based Large Language Models (Video-LLMs) are vulnerable to a universal Energy-Latency Attack (ELA) that triggers a Denial-of-Service (DoS) via spatially concentrated adversarial patches. Because video architectures rely on temporal subsampling and pooling which act as a low-pass filter against full-frame diffuse noise, an attacker can bypass this compression by anchoring cross-modal attention to a dense, localized visual anomaly. By injecting a fixed, content-agnostic patch into the…

A vulnerability in Vision-Language Models (VLMs) relying on shared visual-textual representation spaces allows attackers to induce transferable cross-task semantic failures using an X-shaped Sparse Pixel Attack (XSPA). Attackers craft imperceptible adversarial perturbations restricted to a fixed geometric prior—two intersecting diagonal lines comprising approximately 1.76% of the image pixels. By jointly optimizing a classification objective with cross-task semantic guidance (target-semantic…

Large language models (LLMs) exhibit a "Causal Bypass" vulnerability during Chain-of-Thought (CoT) prompting, where the generated reasoning text does not causally determine the model's final output. Instead of utilizing the explicit CoT tokens, the model routes decision-critical computation through latent, implicit pathways. This allows the visible reasoning trace to function as an unfaithful, post-hoc rationalization rather than an actual representation of the model's internal logic…