Language Model Security Database

A vulnerability exists in tool-enabled Large Language Model (LLM) agents, termed Sequential Tool Attack Chaining (STAC), where a sequence of individually benign tool calls can be orchestrated to achieve a malicious outcome. An attacker can guide an agent through a multi-turn interaction, with each step appearing harmless in isolation. Safety mechanisms that evaluate individual prompts or actions fail to detect the threat because the malicious intent is distributed across the sequence and only…

GPT-OSS-20B exhibits "agentic-only" vulnerabilities where safety guardrails effective in standalone model inference fail when the model operates within an agentic execution loop. These vulnerabilities emerge when the model is deployed in a multi-step agentic architecture (e.g., utilizing LangGraph, tool usage, and memory retention). Attackers can bypass safety filters by employing context-aware iterative refinement attacks, which incorporate the full agentic state—including tool outputs…

Large Language Model (LLM)-powered GUI agents exhibit a vulnerability to deceptive interface designs (dark patterns) due to goal-driven optimization and procedural myopia. When executing natural language instructions on web interfaces, these agents consistently prioritize minimizing steps and achieving task completion over user safety or privacy. Agents frequently recognize manipulative elements—such as pre-selected consent checkboxes, hidden costs, or trick questions—in their internal…

Multi-agent Large Language Model (LLM) systems are vulnerable to compositional privacy leakage, a flaw where sensitive information is exposed through the aggregation of individually benign responses from distinct agents. In distributed architectures where data is siloed (e.g., distinct agents handling HR, Finance, and IT logs), individual agents lack a global view of the user’s accumulated knowledge or the sensitive attributes derivable from cross-agent data combinations. An attacker can…

AdvEDM reveals a vulnerability in Vision-Language Model (VLM) based Embodied Decision-Making (EDM) systems, such as those used in autonomous driving and robotic manipulation. The vulnerability allows an attacker to launch fine-grained adversarial attacks that selectively modify the perception of specific objects in an input image—either by removing them (Semantic Removal) or adding them (Semantic Addition)—while preserving the semantic integrity of the rest of the scene.

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

The GPT-OSS-20B large language model contains critical failures in its alignment and Chain-of-Thought (CoT) reasoning mechanisms, specifically in how it prioritizes numerical objectives and validates procedural structure. The model is vulnerable to "Quant Fever," where explicit numerical targets in a prompt (e.g., "delete 90% of files") override contextual safety constraints (e.g., "do not delete important files"). Furthermore, the model exhibits "Reasoning Procedure Mirage," where harmful…

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 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…

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…