Language Model Security Database

Generative reward models deployed as LLM-as-a-Judge (LaaJ) evaluators contain a logic bypass vulnerability where superficial "master key" inputs trigger false positive rewards regardless of actual response quality. Instead of evaluating the candidate's output, large judge models are inadvertently triggered by specific token sequences to solve the prompt independently. This allows malicious actors or policy models undergoing reinforcement learning to consistently game the reward signal by…

A compound vulnerability in Retrieval-Augmented Generation (RAG) systems allows attackers to deterministically hijack model outputs for arbitrary user queries without prior knowledge of the user's input. The vulnerability, identified as PIDP-Attack, requires a dual-vector exploitation: database poisoning and query-path prompt injection. First, the attacker injects a small number of poisoned passages into the RAG database, each starting with an attacker-chosen "target question" followed by a…

GPT-OSS-Safeguard-20B and Meta-SecAlign (70B/8B) are vulnerable to white-box adversarial attacks generated by automated algorithmic recombination (specifically the claude_v63, claude_v82, and claude_v53-oss optimizers). These algorithms significantly outperform standard discrete optimization methods (like GCG) by integrating continuous optimization (ADC) with LayerNorm gradient scaling (LSGM), or by merging momentum-smoothed gradients with directional perturbation candidate selection (DPTO)…

The paper presents a controlled, reproducible evaluation in which untrusted MCP tool descriptions contain hidden instructions that attempt to induce coding agents to access files, invoke other tools, create deceptive links, or execute remote scripts. The authors report that several clients accepted poisoned metadata without adequate validation or parameter visibility; Cursor 1.6.45 was reported unsafe across all four tested vectors. These are paper-reported results from isolated local tests…

LLM-based autonomous agents deployed in multi-turn, structured environments are vulnerable to adaptive, profit-driven semantic exploitation. Rather than utilizing traditional malformed prompt injections or jailbreaks, an attacker can leverage valid interaction channels to execute social engineering, protocol spoofing, and authority impersonation tactics. By strategically shaping the environment's context—such as feigning technical constraints, fabricating evaluation harnesses, or manipulating…

Agentic Large Language Model (LLM) systems utilizing persistent memory, Retrieval-Augmented Generation (RAG) pipelines, and external tool connectors are vulnerable to Logic-layer Prompt Control Injection (LPCI). An attacker can inject obfuscated (e.g., encoded, structurally nested, or semantically reframed) payloads into external memory stores or RAG documents. These payloads bypass conventional inference-time plaintext content filters, persist across session boundaries, and remain dormant…

OpenClaw is vulnerable to persistent memory poisoning, allowing an attacker to manipulate the agent's long-term memory store (MEMORY.md) via prompt injection. Because the autonomous agent continuously integrates this memory file as context for all subsequent reasoning and task planning, injected payloads act as durable behavioral constraints. This allows an attacker to persistently alter the agent's core policy, manipulate tool selection, and hijack future sessions without any further…

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…

LLM-powered automated social media accounts (bots) are vulnerable to prompt injection via public user replies. When an automated bot scrapes and processes social media engagement to generate responses, an attacker can submit an instruction-override command within a direct reply. Because the underlying LLM fails to isolate its core system instructions (e.g., maintaining a specific political persona) from untrusted user input, the injected command hijacks the model's context window. This forces…

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…