Language Model Security Database

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…

Large Language Models (LLMs) supporting function calling (tool use) are vulnerable to a jailbreak attack known as iMIST (interactive Multi-step Progressive Tool-disguised Jailbreak Attack). The vulnerability stems from a disparity in alignment training: while models are heavily aligned to refuse harmful natural language generation, they lack sufficient alignment regarding the generation of harmful content within structured data (JSON) used for tool parameters.

A vulnerability exists in Large Language Model (LLM) and Large Reasoning Model (LRM) serving interfaces that allow user-defined response prefixes, such as plain text-completion (v1/completions), Fill-in-the-Middle (FIM), or assistant message prefilling. An attacker can perform a Response Prefix Attack (RPA) by injecting maliciously crafted Chain-of-Thought (CoT) reasoning tokens immediately following the assistant's start delimiter (e.g., <|im_start|>assistant). Because these tokens are placed…

Production Large Language Models (LLMs) are vulnerable to long-form training data extraction via a two-phase prompt injection attack. This vulnerability allows an attacker to recover substantial portions of memorized, copyrighted text (such as novels) by exploiting the model's autoregressive text completion capabilities. The attack methodology involves two distinct phases: 1. Prefix Completion Probe: The attacker provides a short "seed" sequence (e.g., the first sentence of a book) coupled…

Large Language Models (LLMs) hosted on inference servers are vulnerable to high-speed weight exfiltration attacks due to the inherent compressibility of transformer parameters when decompression constraints are relaxed. Adversaries with compromised server access can utilize aggressive lossy compression techniques—specifically additive quantization combined with k-means clustering—to reduce model size by factors of 16x to 100x (e.g., <1 bit per parameter). Unlike standard quantization for…

Commercial Multimodal Large Language Model (MLLM) integrated systems are vulnerable to a "Dual Steganography" jailbreak paradigm (referred to as Odysseus). The vulnerability arises from the reliance of safety filters on the assumption that malicious content must be explicitly visible in the input or output modalities (text or image). Attackers can bypass these filters by encoding malicious queries into binary matrices and embedding them into benign-looking images using steganographic encoders…

A Denial-of-Service (DoS) vulnerability exists in Large Language Model (LLM) inference services where specially crafted input prompts can trigger excessively long or infinite generation loops ("infinite thinking"). This vulnerability, identified as "ThinkTrap," utilizes derivative-free optimization (CMA-ES) within a continuous surrogate embedding space to circumvent the discrete nature of token inputs. By optimizing a low-dimensional latent vector and projecting it to token sequences, an…

Reasoning-specialized Large Language Models (LLMs) that utilize Chain-of-Thought (CoT) processes are vulnerable to reasoning-exploitation jailbreaks. Attackers can bypass standard safety alignments (such as RLHF) by using adaptive multi-turn interactions or semantic transformations to induce the model to generate intermediate reasoning steps that "rationalize" or "contextualize" a harmful request. Because current alignment techniques often fail to scale linearly with reasoning depth, forcing…

A safety bypass vulnerability exists in Large Language Models (LLMs) and Multi-Modal Models (LVLMs) that expose a "Prefix Completion" parameter in their inference APIs (e.g., echo or prefix parameters). The vulnerability exploits the model's autoregressive nature, where the model prioritizes local coherence with a user-supplied output prefix over global safety alignment (RLHF) or system instructions. By supplying a prefix that establishes a hostile narrative or intent (e.g., "My actions are as…

Large Language Models (LLMs), specifically GPT-4o, GPT-4o-mini, LLaMA-2-13B, Mistral-7B, and Phi-3.5-mini, are vulnerable to Man-in-the-Middle (MitM) adversarial prompt injections that undermine factual recall. Termed the "$\chi$mera" (Chimera) attack framework, this vulnerability exists when an attacker intercepts and modifies user queries (e.g., via malicious browser extensions, compromised frontends, or proxy middleware) before they reach the victim model. By appending adversarial…