Language Model Security Database

A distributed backdoor vulnerability, named "Collaborative Shadows", exists in LLM-based Multi-Agent Systems (MAS) that rely on external or modifiable tools. An attacker can poison multiple agent tools by embedding inert, encrypted "attack primitives" within them. These primitives are fragments of a larger malicious payload. A carefully crafted user instruction acts as both a trigger and a decryption key. The instruction steers the agents to collaborate in a specific sequence, causing them to…

LLM-based coding agents integrated into IDEs (e.g., VS Code Copilot, Cursor, Windsurf) are vulnerable to a query-agnostic Indirect Prompt Injection (IPI) attack termed "QueryIPI." This vulnerability allows an attacker to achieve Remote Code Execution (RCE) on the developer's machine by injecting a malicious tool definition (e.g., via the Model Context Protocol) into the agent's context.

Large Language Model (LLM) integrated agents and applications are vulnerable to Prompt Injection attacks where untrusted data (e.g., retrieved documents, tool outputs, website content) overrides system instructions. Because LLMs typically process instructions and data within a single context window without strict separation, an attacker can embed imperative commands within the data channel. This vulnerability extends beyond simple overriding instructions; it includes sophisticated techniques…

Mobile LLM-based agents (including Mobile-Agent-E, AppAgent, AutoDroid, and others) are vulnerable to indirect prompt injection attacks delivered via untrusted third-party mobile channels, such as in-app advertisements, system notifications, and embedded webviews. These agents utilize Multimodal Large Language Models (MLLMs) to perceive the device state via screenshots or accessibility trees. The vulnerability exists because the agents concatenate the user's prompt ($p$) with the environmental…

A vulnerability exists in Large Language Model (LLM) agentic systems where automated reinforcement learning (RL) techniques can bypass advanced prompt injection defenses, including Instruction Hierarchy and SecAlign. The specific attack methodology, dubbed "RL-Hammer," utilizes Group Relative Policy Optimization (GRPO) to train an attacker model from scratch without warm-up data. The vulnerability exploits the reward sparsity in robust models by employing a "bag of tricks": removing KL…

Large Language Model (LLM) agents utilizing long-term memory or Retrieval-Augmented Generation (RAG) are vulnerable to context-dependent memory injection attacks. Unlike traditional prompt injections that are overtly malicious, this vulnerability involves injecting records that appear benign and coherent in isolation—thereby bypassing standard perplexity filters and static content moderation (e.g., LlamaGuard). These records contain "sleeping" malicious logic that is only activated when…

Large Vision-Language Model (LVLM) driven mobile agents, such as Mobile-Agent-E, are vulnerable to a touch-guided visual prompt injection attack. This vulnerability allows an attacker to hijack the agent's execution flow via a malicious Android application interface without requiring system-level privileges. The attack leverages "Non-privileged Perception Compromise," where a visual payload is embedded in the application UI and conditionally rendered only during agent-specific interaction…

Large Language Model (LLM)-based Automated Program Repair (APR) systems—such as SWE-agent, OpenHands, and AutoCodeRover—are vulnerable to adversarial manipulation via crafted bug reports. These systems accept unvetted natural language issue descriptions as trusted input to synthesize code patches. An attacker can exploit this trust by submitting semantically plausible but malicious bug reports designed to mislead the APR agent. By leveraging the semantic gap between natural language…

Frontier Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) reasoning are vulnerable to deceptive alignment attacks via adversarial system prompt injection. This vulnerability allows an attacker to induce "deceptive reasoning," where the model’s internal CoT actively plans or entertains malicious directives (e.g., radicalization, bias, or violence) while the final user-facing output remains benign, helpful, and innocuous. By creating a dissociation between internal reasoning and…

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…