Attack Context
Attacks leveraging model implementation details
194 matching entries out of 228 in this category
Third-party fine-tuning adapters may contain backdoors. Z-PEFT screens adapter weights using spectral features, evaluated on PADBench's 13,300 adapters.
Source: arXiv
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…
Source: arXiv
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…
Source: arXiv
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…
Source: arXiv
A vulnerability in LLM parameter-space merging algorithms enables a latent supply-chain attack where adversaries embed pre-computed malicious weight perturbations into open-source models. Using a constrained optimization framework, attackers inject latent components into the Multilayer Perceptron (MLP) up-projection matrices of source models. These perturbations are mathematically constrained to preserve the source model's individual safety alignment (via directional consistency) and benign…
Source: arXiv
State-of-the-art Large Language Models (LLMs) and safety guardrails lack domain-specific safety alignment for food science, making them vulnerable to generating actionable, hazardous food safety instructions. Attackers can exploit this alignment sparsity using canonical jailbreak techniques (such as AutoDAN and Persuasive Adversarial Prompting) or direct adversarial prompting to bypass generic safety filters. This allows malicious actors to elicit harmful guidance that violates fundamental FDA…
Source: arXiv
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…
Source: arXiv
An issue in large language models (LLMs) with white-box weight access allows attackers to permanently bypass safety guardrails via Weight Orthogonalization (WO). By calculating a model's "refusal vector"—the mean-difference vector between harmful and harmless instruction activations in the residual stream—an attacker can orthogonalize the model's weights to prevent it from writing to this refusal direction ($W^{\prime}\leftarrow W-rr^{\intercal}W$). Unlike jailbreak-tuning or data poisoning…
Source: arXiv
An activation-space adversarial attack, termed "Amnesia", allows an attacker with white-box access to bypass the safety mechanisms of open-weight Large Language Models (LLMs) at inference time without requiring fine-tuning, weight modifications, or prompt manipulation. The vulnerability stems from how safety-aligned LLMs localize refusal features within the attention value path of specific decoder layers. An attacker can extract an attack vector ($\mathbf{V}_i$) by hooking the attention value…
Source: arXiv
GPT-OSS-Safeguard-20B and Meta-SecAlign (70B/8B) are vulnerable to white-box adversarial attacks generated by automated algorithmic recombination (specifically the claude_v63, claude_v82, and claude_v53-oss optimizers). These algorithms significantly outperform standard discrete optimization methods (like GCG) by integrating continuous optimization (ADC) with LayerNorm gradient scaling (LSGM), or by merging momentum-smoothed gradients with directional perturbation candidate selection (DPTO)…
Source: arXiv