Language Model Security Database

Large Language Model (LLM) inference-time watermarking schemes are vulnerable to evasion via character-level perturbations that disrupt the model's tokenizer. Unlike token-level attacks (e.g., synonym replacement), character-level edits—such as homoglyph substitutions, zero-width character insertions, and typos—force the tokenizer to segment a single semantic unit into multiple sub-word tokens. This fragmentation alters the context window used by the watermarking hashing function (e.g., the…

A vulnerability exists in the alignment mechanisms of Large Language Models (LLMs) where activation steering—the process of injecting vectors into hidden states during inference—can systematically bypass refusal safeguards. By modifying the residual stream activations at intermediate layers (typically $\lfloor L/2 \rfloor$) using the formula $\overline{\mathbf{x}}_{i}^{(l)}=\mathbf{x}_{i}^{(l)}+\alpha\mathbf{v}$, an attacker can force the model to comply with harmful requests. Research…

A vulnerability exists in Vision-Language Models (VLMs) that allows for the bypass of safety alignment mechanisms through loss-guided adversarial image perturbations. This attack, known as JaiLIP, operates entirely in the image space, requiring no textual prompt manipulation. The vulnerability is exploited by optimizing an adversarial image using a joint objective function that minimizes the Mean Squared Error (MSE) between the clean and perturbed image while maximizing the model's loss for…

A vulnerability exists in multiple Large Language Models (LLMs) that allows for safety alignment bypass through a technique named Activation-Guided Local Editing (AGILE). The attack uses white-box access to a source model's internal states (activations and attention scores) to craft a transferable text-based prompt that elicits harmful content.

Large Language Models (LLMs) and Vision-Language Models (VLMs) are vulnerable to an automated, adaptive role-play jailbreak attack known as GUARD (Guideline Upholding Test through Adaptive Role-play and Jailbreak Diagnostics). The vulnerability exists because the models fail to recognize malicious intent when harmful queries are embedded within complex, iteratively optimized "playing scenarios."

A vulnerability exists in the tool selection mechanisms of Large Language Model (LLM) agents, identified as the "Attractive Metadata Attack" (AMA). This flaw allows an adversary to manipulate the metadata (names, descriptions, and parameter schemas) of malicious external tools to statistically maximize the likelihood of their selection by the agent, without requiring prompt injection or access to model internals. The vulnerability exploits the agent’s semantic scoring function used to map user…

Large Language Models (LLMs) exposed via public APIs are vulnerable to model fingerprinting attacks where an attacker can identify the exact backend model family and version (e.g., distinguishing Mistral-7B-v0.1 from v0.3) by analyzing response patterns. While traditional fingerprinting relies on manual query curation, this vulnerability is exacerbated by Reinforcement Learning (RL) based query optimization. An attacker can train an RL agent (specifically using Proximal Policy Optimization) to…

Large Language Models (LLMs) are vulnerable to automated adversarial attacks that systematically combine multiple jailbreaking "primitives" into complex prompt chains. A dynamic optimization engine can generate and test billions of unique combinations of techniques (e.g., low-resource language translation, payload splitting, role-playing) to bypass safety guardrails. This combinatorial approach differs from manual red-teaming by systematically exploring the attack surface, achieving…

Large Reasoning Models (LRMs) can be instructed via a single system prompt to act as autonomous adversarial agents. These agents engage in multi-turn persuasive dialogues to systematically bypass the safety mechanisms of target language models. The LRM autonomously plans and executes the attack by initiating a benign conversation and gradually escalating the harmfulness of its requests, thereby circumventing defenses that are not robust to sustained, context-aware persuasive attacks. This…

Multimodal Large Language Models (MLLMs) are vulnerable to a jailbreak attack strategy known as Balanced Structural Decomposition (BSD). This vulnerability exploits a structural trade-off in safety alignment where models fail to detect malicious intent when the input balances semantic relevance ("On-Topicness") with distributional novelty ("OOD-Intensity"). The attack functions by recursively decomposing a harmful text objective into a tree of sub-tasks using an "Explore" (diversity) and…