Language Model Security Database

A stored interaction can later steer a memory-augmented agent's answer without direct memory-store access. The study evaluates persistent response manipulation in MemoryOS and MemGPT.

The paper presents a concrete, reproducible security evaluation in which attacker-controlled instructions embedded in retrieved external content steer stateful, tool-calling LLM agents toward unauthorized actions. It adapts white-box GCG and black-box TAP to AgentDojo and evaluates single-task and task-universal attacks across 80 task pairs in four domains. The reported results show that semantic black-box optimization can discover functional prompt injections more effectively than…

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…

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…

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)…

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…

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…

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…

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…

Large Language Models (LLMs) are vulnerable to Attribute Inference Attacks, where an attacker exploits the model's reasoning capabilities to deduce sensitive personal attributes (e.g., age, gender, location, income level) from seemingly innocuous, unclassified user-generated text. Unlike traditional privacy leaks that rely on the memorization of training data, this vulnerability leverages the model's zero-shot inference and contextual deduction. Because the attack prompts are benign in nature…