Language Model Security Database

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…

Large Language Models (LLMs) utilized for static code analysis, code review, and autonomous software engineering exhibit a cognitive vulnerability termed "Abstraction Bias." When processing code that structurally resembles common algorithmic patterns (e.g., standard sorting algorithms, helper functions, or mathematical formulas), the model relies on high-level memorized representations of the algorithm's intent rather than analyzing the specific local logic. Adversaries can exploit this by…

A vulnerability exists in the graph encoding architecture of LLaGA (Large Language and Graph Assistant), specifically within the "neighborhood detail template" used to construct node sequences. LLaGA enforces a fixed-shape computational tree for each node; when a target node has fewer neighbors than the required template size (e.g., $k$ children), the system utilizes placeholders to maintain the fixed structure.

Multi-tenant Large Language Model (LLM) inference systems utilizing global Key-Value (KV) cache sharing are vulnerable to a timing side-channel attack. By measuring the Time-To-First-Token (TTFT) latency of crafted API requests, an unprivileged remote attacker can determine if specific token sequences have been previously processed and cached by the system for other users. This observable timing difference between cache hits (low TTFT) and cache misses (high TTFT) allows for the token-by-token…

A Time-of-Check to Time-of-Use (TOCTOU) vulnerability exists in LLM-enabled agentic systems that execute multi-step plans involving sequential tool calls. The vulnerability arises because plans are not executed atomically. An agent may perform a "check" operation (e.g., reading a file, checking a permission) in one tool call, and a subsequent "use" operation (e.g., writing to the file, performing a privileged action) in another tool call. A temporal gap between these calls, often used for LLM…

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…