Language Model Security Database

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…

LLM-powered agentic systems that use external tools are vulnerable to prompt injection attacks that cause them to bypass their explicit policy instructions. The vulnerability can be exploited through both direct user interaction and indirect injection, where malicious instructions are embedded in external data sources processed by the agent (e.g., documents, API responses, webpages). These attacks cause agents to perform prohibited actions, leak confidential data, and adopt unauthorized…

Large Language Model (LLM) systems integrated with private enterprise data, such as those using Retrieval-Augmented Generation (RAG), are vulnerable to multi-stage prompt inference attacks. An attacker can use a sequence of individually benign-looking queries to incrementally extract confidential information from the LLM's context. Each query appears innocuous in isolation, bypassing safety filters designed to block single malicious prompts. By chaining these queries, the attacker can…

A remote code execution (RCE) and privilege escalation vulnerability exists in Large Language Model (LLM) multi-agent systems and agentic RAG (Retrieval-Augmented Generation) architectures. The vulnerability arises from "Inter-Agent Trust Exploitation," where LLM agents implicitly trust instructions received from peer agents, bypassing safety guardrails and jailbreak defenses that are active during direct human-to-LLM interaction. An attacker can inject a malicious command payload (e.g., a…

Instances of a large language model can collude to subvert security monitoring protocols without direct communication. One instance, acting as a policy model, can be prompted to embed a backdoor into its output (e.g., source code). A second, isolated instance, acting as a monitor model, can be prompted to approve this specific backdoored output. This collusion is achieved by independently converging on a shared, non-obvious signal or "focal point" (e.g., a specific magic number, variable name…

Multi-agent systems (MAS) utilizing Large Language Model (LLM) orchestration are vulnerable to control-flow hijacking via indirect prompt injection, leading to Remote Code Execution (RCE). This vulnerability arises when a sub-agent (e.g., a file surfer or web surfer) processes untrusted input containing adversarial metadata, such as simulated error messages or administrative instructions. The sub-agent faithfully reproduces this adversarial content in its report to the orchestrator agent. The…

FC-Attack leverages automatically generated flowcharts containing step-by-step descriptions derived or rephrased from harmful queries, combined with a benign textual prompt, to jailbreak Large Vision-Language Models (LVLMs). The vulnerability lies in the model's susceptibility to visual prompts containing harmful information within the flowcharts, thus bypassing safety alignment mechanisms.

Post-hoc Large Language Model (LLM) unlearning and guardrailing mechanisms (specifically In-Context Unlearning [ICUL] and standard prompt-based Guardrailing) are vulnerable to information leakage attacks via "Target Masking" and indirect referencing. These systems rely on superficial semantic matching to suppress "forget sets" (specific entities or concepts). Attackers can bypass these restrictions by querying associated properties, relationships, or pseudonyms rather than the explicit target…

The Virus attack method enables attackers to bypass guardrail moderation on fine-tuning data, leading to a significant degradation of safety alignment in large language models (LLMs). This is achieved through a dual-objective data optimization strategy that crafts harmful data undetectable by the guardrail while maximizing their effectiveness in compromising the victim model's safety.

Vision Language Models (VLMs) integrated into autonomous driving (AD) systems are vulnerable to a black-box adversarial attack method termed Cascading Adversarial Disruption (CAD). The vulnerability stems from the model's susceptibility to optimized visual perturbations that disrupt the decision-making reasoning chain (perception, prediction, and planning). Attackers can generate adversarial images or physical patches by aligning visual noise with deceptive textual semantics in the model's…