Language Model Security Database

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…

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…

Hybrid monitoring protocols for Large Language Model (LLM) agents, specifically those that strictly evaluate both Chain-of-Thought (CoT) reasoning and tool usage (such as LlamaFirewall's AlignmentCheck and Extract-and-Evaluate monitors), are vulnerable to "Agent-as-a-Proxy" indirect prompt injection attacks. Unlike standard injections that target the agent, this attack targets the monitoring model itself. By embedding a malicious string—optimized via an algorithm named Parallel-GCG—into…

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…

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…

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…

A logical vulnerability exists in LLM security proxies and guardrails that utilize weighted-average aggregation algorithms for multi-turn risk scoring. The scoring logic exhibits a mathematical "ceiling property" where the cumulative conversation-level risk score converges to the per-turn score regardless of the number of interaction turns ($n$). Consequently, the aggregated score is bounded by the maximum single-turn score ($cum \leq \max(s_i)$). This allows remote attackers to bypass…

OpenClaw is vulnerable to Indirect Prompt Injection (IPI), Tool-Return Manipulation, and Persistent Memory Poisoning. The agent incorporates untrusted external content (e.g., fetched web pages) and external tool outputs directly into its observation stream without sufficient isolation. An attacker can embed malicious payloads into these external channels to hijack the agent's planning and execution trace. This allows the attacker to silently trigger high-privilege actions via OpenClaw's Skills…

LLM-as-a-Judge systems utilizing natural language rubrics are vulnerable to Rubric-Induced Preference Drift (RIPD). This vulnerability allows an attacker (or a flawed optimization process) to refine evaluation rubrics such that they maintain high agreement with human references on standard validation benchmarks while inducing systematic, directional preference degradation on unseen target domains. The attack exploits the disconnect between benchmark validation and target generalization by…