Language Model Security Database

LLM-based search agents are vulnerable to manipulation via unreliable search results. An attacker can craft a website containing malicious content (e.g., misinformation, harmful instructions, or indirect prompt injections) that is indexed by search engines. When an agent retrieves and processes this page in response to a benign user query, it may uncritically accept the malicious content as factual and incorporate it into its final response. This allows the agent to be used as a vector for…

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 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) 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…

Large language models that support a developer role in their API are vulnerable to a jailbreaking attack that leverages malicious developer messages. An attacker can craft a developer message that overrides the model's safety alignment by setting a permissive persona, providing explicit instructions to bypass refusals, and using few-shot examples of harmful query-response pairs. This technique, named D-Attack, is effective on its own. A more advanced variant, DH-CoT, enhances the attack by…

IntentionReasoner, specifically the 1.5B and 3B parameter versions optimized via Reinforcement Learning (RL), contains a safety regression vulnerability where the RL alignment process degrades the model's resistance to jailbreak attacks compared to the Supervised Fine-Tuning (SFT) baseline. While RL improves general utility and rewriting quality, it inadvertently increases the Attack Success Rate (ASR) for adversarial inputs in smaller architectures. This allows sophisticated jailbreak prompts…