Language Model Security Database

A side-channel information leakage vulnerability exists in the "locate-then-edit" paradigm of Large Language Model (LLM) knowledge editing, specifically affecting algorithms such as ROME, MEMIT, and AlphaEdit. The parameter update matrix ($\Delta W$) generated during the editing process preserves the algebraic structure of the edited data. Specifically, the row space of the parameter difference matrix encodes a mathematical fingerprint of the key vectors associated with the edited subjects. An…

Large Language Models (LLMs) are vulnerable to Attribute Inference Attacks, where an attacker exploits the model's reasoning capabilities to deduce sensitive personal attributes (e.g., age, gender, location, income level) from seemingly innocuous, unclassified user-generated text. Unlike traditional privacy leaks that rely on the memorization of training data, this vulnerability leverages the model's zero-shot inference and contextual deduction. Because the attack prompts are benign in nature…

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…

The Model Context Protocol (MCP) architecture lacks a semantic verification mechanism to enforce consistency between a tool's documented behavior (exposed to the Large Language Model via JSON schemas) and its actual executable logic. This design gap allows MCP Servers to present benign, read-only, or limited-scope descriptions to the LLM agent while implementing undocumented, privileged, or state-mutating functionality in the underlying code. An attacker can exploit this description–code…

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…

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…

Retrieval-Augmented Generation (RAG) systems are vulnerable to iterative knowledge-extraction attacks designed to reconstruct the underlying private knowledge base. The vulnerability exists due to the decoupled optimization of the retrieval and generation phases. Attackers can craft adversarial queries consisting of two distinct components: an "Information" component (optimized via gradient descent or random sampling to steer embeddings toward specific, diverse regions of the vector space) and…

A vulnerability exists in tool-augmented Large Language Model (LLM) agents characterized as "Tag-Along Attacks," where an unprivileged external user (or adversarial agent) coerces a safety-aligned Operator agent into executing prohibited tool calls. Unlike Indirect Prompt Injection, this attack targets the direct conversational interface using a technique termed "Imperative Overloading." By mimicking system prompt syntax and utilizing high-priority imperative commands (e.g., "Strict adherence…

Audio Large Language Models (ALLMs) integrated into voice agent systems for high-stakes domains (banking, IT support, logistics) are vulnerable to multimodal adversarial attacks via spoken interaction. Adversaries can exploit the model's inherent compliance and contextual awareness through multi-turn dialogue to bypass authentication safeguards, escalate privileges (e.g., unauthorized credit limit increases), exfiltrate sensitive Personally Identifiable Information (PII), and poison…

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…