Language Model Security Database

Multi-Agent Systems based on Large Language Models (LLM-MAS) are vulnerable to systemic Consensus Corruption via cascading error amplification. Because mainstream collaborative architectures rely on recursive context reuse without atomic-level provenance tracking, a single atomic falsehood injected into the system is repeatedly cited and reused within the multi-agent interaction chain. This structural exposure causes the error to deterministically compound across the communication graph…

Internal Safety Collapse (ISC) is a vulnerability in frontier Large Language Models (LLMs) where models autonomously generate highly restricted, harmful content while executing structurally legitimate professional workflows. The vulnerability triggers when a model infers that generating sensitive data is a functional requirement to complete an otherwise benign task. By nesting harmful content generation inside standard execution constraints (e.g., resolving a schema validation error in a…

Embodied Large Language Models (LLMs) used for real-world agent planning are vulnerable to Action-level Manipulation (dubbed "Blindfold"), a jailbreak technique that bypasses semantic-level safety filters by exploiting the models' limited spatial and causal reasoning regarding physical consequences. Attackers can use an adversarial proxy LLM to decompose a semantically harmful intent into a sequence of individually benign primitive actions. To evade advanced semantic correlation checks…

Agentic LLM systems that automatically preview URLs or extract web metadata are vulnerable to implicit prompt injection, resulting in silent data exfiltration ("silent egress"). Attackers can embed adversarial instructions in unobserved web elements, such as HTML <title> tags, <meta> descriptions, or Open Graph metadata. When a user requests a summary of the URL—or when the agent automatically unfurls a linked URL in a chat—the system fetches the malicious page and flattens this metadata into…

Agentic LLMs integrated with external data services (e.g., Model Context Protocol, MCP) are vulnerable to Adaptive Indirect Prompt Injection (IPI) attacks. When an agent queries external servers, attackers can inject malicious payloads into the retrieved content to hijack the agent's reasoning process and force the execution of high-authority tools. Unlike traditional static prompt injections, this vulnerability dynamically exploits the agent's internal logic audit. By using Markovian…

A vulnerability in LLM-based Multi-Agent Systems (MAS) allows an attacker to propagate covert biases and misalignment across multiple agents via subliminal prompting, an attack vector termed "Thought Virus." By injecting a seemingly benign, semantically unrelated token (such as a specific 3-digit number) into the prompt of a single compromised agent, an attacker can induce a specific targeted behavior (e.g., outputting a specific target concept or decreasing factual truthfulness). This induced…

The paper describes a reproducible prompt-injection failure in which low-privilege user or tool-output text that imitates a trusted role—especially a model’s reasoning style—is treated as authoritative. Its zero-shot, black-box “CoT Forgery” evaluation injects fabricated reasoning into user prompts and retrieved webpage/tool output. The authors report 60% average attack success on StrongREJECT and 56–70% success in an agent data-exfiltration evaluation, versus near-zero or substantially lower…

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…

A vulnerability exists in LLM-based coding agents that implement modular capability extensions (often referred to as "Agent Skills") where the agent dynamically loads and executes user-provided skill packages. The vulnerability allows for Skill-Based Prompt Injection, specifically leveraging a technique known as "SkillJect." This attack decouples the malicious intent from the operational payload to bypass semantic safety filters. An attacker constructs a skill package containing: 1. Inducement…

Mobile Large Language Model (LLM) agents operating under the "Screen-as-Interface" paradigm are vulnerable to visual indirect prompt injection and state desynchronization. Agents that rely on unstructured visual data (screenshots) and Accessibility Service APIs to perceive the environment lack a mechanism to distinguish between trusted system UI elements and untrusted content (e.g., web pages, emails, or malicious overlays). An attacker can inject visual cues, fake notifications, or hidden…