Language Model Security Database

A prompt structure vulnerability exists in instruction-tuned Large Language Models (LLMs) where attackers can bypass safety alignments by injecting a continuation-triggering suffix immediately following the user prompt termination token. By placing an affirmative suffix outside the user instruction boundary, it is processed as the beginning of the assistant's own pre-filled response. This structural manipulation intrinsically overactivates the model's continuation attention heads, forcing its…

Spoken Language Models (SLMs) are vulnerable to Joint Audio-text Multimodal Attacks (JAMA), which bypass safety alignments by simultaneously perturbing both text and audio inputs. The vulnerability exploits the combined optimization of a discrete text suffix via Greedy Coordinate Gradient (GCG) and a continuous audio perturbation via Projected Gradient Descent (PGD). This joint gradient-based attack pushes the model's hidden layer representations into a distinct subspace far from the benign…

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…

A vulnerability in safety-aligned open-weights Large Language Models (LLMs) allows attackers to bypass refusal mechanisms during inference via a representation-level jailbreak known as PCA-regularized Gaussian Optimal Transport (PCA-OT). Unlike previous 1D directional ablation methods (e.g., Refusal Feature Ablation), this attack computes a minimal-cost optimal transport map that matches both the mean and covariance of "harmful" activation distributions to "harmless" ones within a…

A vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to achieve an exponentially scaling Attack Success Rate (ASR) for jailbreaks by combining adversarial prompt injection with repeated inference-time sampling. While ASR against un-injected prompts scales polynomially with the number of generated samples ($k$), introducing a long adversarial suffix acts as a strong "misalignment field." This shifts the model's generation distribution into a replica-symmetric ordered…

Activation steering techniques, such as Contrastive Activation Addition (CAA), systematically erode the safety alignment of Large Language Models (LLMs) due to geometric interference within the residual stream. Steering vectors intended to modulate benign or utility-driven behaviors (e.g., sycophancy, openness, self-awareness) often exhibit a negative cosine similarity with the model's latent 1D refusal direction. When applied during inference, these steering vectors inadvertently suppress the…

Large Language Models (LLMs) are vulnerable to TAO-Attack, an advanced optimization-based jailbreak that bypasses safety alignments by exploiting gradient-guided token updates. The vulnerability stems from a two-stage loss function combined with a Direction-Priority Token Optimization (DPTO) algorithm. In the first stage, the attack optimizes an adversarial prompt suffix to minimize the probability of refusal signals (e.g., "I cannot") while maximizing the probability of a harmful target…

The integration of the visual modality in Large Vision-Language Models (VLMs) introduces a vulnerability where appending an image to a harmful text prompt induces a "jailbreak-related representation shift" in the model's internal high-dimensional space. This shift forcibly steers the model's last-token hidden state away from a designated refusal state and into a distinct jailbreak state. The vulnerability occurs because the visual modality overrides the safety alignment of the underlying…

Activation-delta-based linear probes used for detecting task drift and prompt injections in Large Language Models (LLMs) can be bypassed using universal adversarial suffixes. By utilizing the Greedy Coordinate Gradient (GCG) algorithm, an attacker can generate a single, optimized suffix that simultaneously fools multiple logistic regression classifiers attached to different hidden layers of the LLM. The attack jointly optimizes the suffix tokens by accumulating gradients from the losses of all…