Language Model Security Database

A vulnerability exists in MetaGPT's DataInterpreter agent (and similar RAG-based agents utilizing persistent long-term memory) that allows for persistent memory poisoning via indirect injection. The vulnerability exploits the agent's "semantic imitation heuristic," where the agent blindly trusts and imitates retrieved past experiences. An attacker can supply a benign-looking artifact (e.g., a README file or documentation) containing executable code blocks or structured text that the agent…

A vulnerability exists in Multi-Agent Systems (MAS) utilizing Large Language Models (LLMs) for clinical decision support, specifically affecting architectures where a central "AI Doctor" agent relies on consensus or majority voting from assistant agents. The vulnerability, termed "Many-to-One Adversarial Consensus," allows a coalition of colluding adversarial agents to override the central agent's safety alignment and internal knowledge. By fabricating a false consensus (typically requiring $k…

A vulnerability exists in large language models where safety guardrails can be bypassed by decomposing a single harmful objective into a sequence of individually innocuous sub-queries. An attacker agent can use an adaptive tree search algorithm (Correlated Knowledge Attack Agent - CKA-Agent) to explore the target model's internal correlated knowledge. The agent issues benign queries, uses the model's responses to guide exploration along multiple reasoning paths, and aggregates the collected…

Instruction-tuned Large Language Models (LLMs) employing Reinforcement Learning from Human Feedback (RLHF) contain a behavioral vulnerability arising from "over-optimized social priors." This vulnerability, termed Psychological Jailbreak, allows attackers to bypass safety guardrails by exploiting the model’s optimization for anthropomorphic consistency. By establishing a Structured Persona Context (SPC) that aligns with latent psychometric traits (e.g., high agreeableness or neuroticism), an…

Large Language Model (LLM) agents utilizing the Model Context Protocol (MCP) are vulnerable to semantic injection attacks via adversarial tool descriptors. The vulnerability arises because MCP implementations inject natural language tool metadata (descriptions, schemas) directly into the model's reasoning context without semantic sanitization or cryptographic binding. This allows unprivileged adversaries to register tools containing hidden imperative instructions within the descriptor text…

Implementations of Large Language Model (LLM) watermarking algorithms—specifically KGW (Kirchenbauer et al.), Semantic Invariant Robust (SIR) Watermark, Entropy-based Text Watermarking (EWD), and Unbiased Watermarking—are vulnerable to watermark stripping via adversarial text perturbation. When watermarked text generated by models such as OPT-1.3B is subjected to automated paraphrasing or back-translation (e.g., English $\to$ French $\to$ English), the embedded statistical signals are…

Large Language Models are vulnerable to a conceptual manipulation attack, termed Morphology Inspired Conceptual Manipulation (MICM), that bypasses standard safety filters to generate content aligned with harmful extremist ideologies. The attack does not use explicit keywords or standard jailbreak syntax. Instead, it embeds a curated set of seemingly innocuous phrases, called Concept-embedded Triggers (CETs), into a prompt template. These CETs represent an abstract "conceptual configuration" of…

A jailbreak vulnerability, termed Embedded Jailbreak Template (EJT), allows for the generation of harmful content by bypassing the safety mechanisms of Large Language Models (LLMs). The attack uses a generator LLM to contextually integrate a harmful query into a pre-existing jailbreak template. Unlike fixed templates which insert a query into a static placeholder, EJT rewrites multiple parts of the template to embed the harmful intent naturally. This process preserves the original template's…

Large Language Models (LLMs) are vulnerable to a novel class of jailbreak attacks generated through the evolutionary synthesis of executable, code-based attack algorithms. Unlike traditional methods that refine or combine static prompts, this technique uses an automated multi-agent system (EvoSynth) to autonomously engineer and evolve the underlying code that generates the attack. These generated algorithms exhibit high structural and dynamic complexity, using features like control flow, state…

A vulnerability exists in aligned Large Language Models (LLMs) that can be exploited by the FORGEDAN evolutionary framework to bypass safety and alignment mechanisms. The attack, which operates in a black-box setting, uses a genetic algorithm to automatically evolve effective jailbreak prompts. The framework combines multi-strategy textual perturbations (at the character, word, and sentence levels) with a semantic fitness function based on RoBERTa embeddings. This allows it to iteratively…