Language Model Security Database

Instruction-tuned Large Language Models (LLMs) exhibit a selective safety alignment vulnerability, allowing attackers to bypass hate speech filters by shifting the demographic target of an adversarial prompt. Current safety guardrails do not generalize the semantic concept of harm, but instead memorize refusal boundaries for high-visibility demographic groups (e.g., Black, LGBTQIA+). Attackers can successfully generate targeted hate speech by directing identical jailbreak vectors at…

A vulnerability in the safety alignment of several major Large Language Models (LLMs) allows attackers to bypass content filters using complex, automatically generated adversarial prompts. Discovered via the AgenticRed evolutionary framework, the flaw is exploited by wrapping malicious intents in structured formats (such as strict JSON output contracts), combined with prefix injection and refusal suppression. By explicitly commanding the model to begin its response with a compliant prefix and…

Semantic caching mechanisms in LLM applications are vulnerable to cross-tenant cache key collision attacks (CacheAttack) due to the inherent mathematical conflict between locality-preserving fuzzy hashing and cryptographic collision resistance (the avalanche effect). An attacker can leverage gradient-based search algorithms to optimize an adversarial discrete suffix that, when appended to a malicious prompt, forces its output embedding vector to collide with the embedding of a targeted benign…

Large Language Models (LLMs) exhibit a vulnerability to "hard-to-falsify" deceptive evidence injection, termed the "Facade of Truth." This vulnerability allows an attacker to override an LLM’s parametric knowledge (internal factual beliefs) by injecting sophisticated, iteratively refined fabricated evidence into the context window. Unlike overt misinformation which models typically reject, this attack utilizes a multi-agent adversarial framework (MisBelief) to generate evidence that mimics…

Large Language Models (LLMs) and their associated output guardrails (e.g., Llama Guard, OpenAI Moderation API) rely on autoregressive, token-by-token processing, which interprets text as a one-dimensional sequence. A vulnerability exists wherein harmful content can bypass these safety filters by exploiting the discrepancy between 1D token serialization and 2D visual rendering. By redistributing tokens across different rows, columns, or diagonals (SpatialJB), attackers can induce the model to…

A stealthy resource exhaustion (Economic Denial-of-Service) vulnerability exists in the multi-turn tool-calling layer of Large Language Model (LLM) agents, particularly those utilizing the Model Context Protocol (MCP). An attacker controlling a third-party tool server can manipulate text-visible fields (such as argument descriptions and error messages) to force the LLM into a prolonged, verbose tool-calling loop. By demanding lengthy, non-semantic outputs (e.g., long comma-separated lists) and…

Large Language Model (LLM) agents utilizing external tool execution frameworks are vulnerable to Indirect Prompt Injection (IPI) via the "Tool Stream." Unlike traditional data-stream injections (e.g., malicious emails), this vulnerability exploits the agent's interpretation of functional tool definitions (docstrings, signatures) and runtime feedback (error messages, return values) as binding operational constraints. Adversaries functioning as compromised or malicious tool providers can embed…

A cognitive vulnerability exists in the reasoning mechanisms of autonomous Large Language Model (LLM) agents, specifically regarding "narrative overfitting"—the model's intrinsic drive to synthesize coherent causal stories from fragmented inputs. This vulnerability allows for "Cognitive Collusion Attacks" where an attacker creates a fabricated belief state in the victim agent using exclusively factually true evidence fragments. By employing a "Generative Montage" framework (consisting of…

Large Language Model (LLM) agents implementing the Model Context Protocol (MCP) are vulnerable to Implicit Tool Poisoning (ITP). This vulnerability allows an attacker to manipulate agent behavior by embedding malicious instructions within the metadata (specifically the natural language description) of a third-party tool. Unlike explicit tool poisoning, where the agent is tricked into invoking a malicious tool, ITP exploits the agent's contextual reasoning to force the invocation of a distinct…

Closed-source Multi-modal Large Language Models (MLLMs) are vulnerable to Universal Targeted Transferable Adversarial Attacks (UTTAA). An attacker can generate a single, image-agnostic adversarial perturbation ($\delta$) that, when added to any arbitrary source image, steers the victim model to output a description or classification matching a specific target image chosen by the attacker. This vulnerability exploits the transferability of adversarial features from open-source surrogate vision…