Language Model Security Database

The paper describes and evaluates a reproducible application-layer weakness in agents with persistent memory: untrusted external content can cross the memory-write boundary, be stored as trusted factual, experience, or procedural memory, and influence later sessions. It identifies four write channels—explicit writes, policy-driven writes, compaction, and experience-to-procedure synthesis—and six attack classes. For safe defensive testing, use MPBench’s two-phase structure in an isolated agent…

SilentRetrieval describes a specific RAG corpus-integrity vulnerability: an attacker able to add a topically relevant document to a retrieval corpus can make that document rank highly and influence the generated answer while remaining fluent enough to evade simple perplexity checks. The paper evaluates a two-stage method combining retrieval-oriented document optimization with context-adaptive claim integration. A safe defensive reproduction is to use only isolated benchmark corpora and inert…

The paper presents a specific black-box indirect prompt-injection evaluation: attacker-controlled external content can cause a memory-enabled assistant or external memory manager to persist a fabricated user memory, which may later be retrieved in a separate session and steer responses or agent actions. The authors evaluate injection, retrieval, and conditional adversarial usage separately across synthetic document and future-session datasets. The released repository provides defensive…

The paper describes a specific black-box knowledge-base poisoning attack against retrieval-augmented reasoning systems. An attacker who can add one query-specific document to the corpus can cause it to be retrieved and influence the LLM toward an attacker-chosen answer. AdversarialCoT shapes the document around the target model’s observable reasoning structure and iteratively refines retrieval relevance and persuasive reasoning using final model feedback. The paper reports this as an…

LLM-based coding agents are vulnerable to Document-Driven Implicit Payload Execution (DDIPE) via supply-chain poisoning of third-party agent skills. Attackers can embed malicious logic directly into legitimate-looking code examples and configuration templates within skill documentation files (e.g., SKILL.md). Because coding agents ingest this metadata into their context windows and treat the documentation as an authoritative reference, the underlying LLM silently reproduces and executes the…

LLM-based personal agents are vulnerable to Indirect Prompt Injection (IPI) defense bypasses via declarative context reframing and implicit file provenance trust. Attackers can bypass agent safety filters by phrasing malicious instructions as declarative compliance alerts rather than imperative commands. Because agents are designed to report discrepancies as expected behavior, declarative framing bypasses intent-sensitive safety mechanisms. Additionally, attackers can exploit the agent's…

Autonomous LLM agents deployed in dynamic, multi-step tool-calling environments are highly vulnerable to Indirect Prompt Injections (IPI) embedded in external content. Surface-level defensive prompts and monitoring mechanisms (such as Prompt Warning, the Sandwich Method, Spotlighting, Keyword Filtering, and LLM-as-a-Judge) consistently fail to prevent exploitation and occasionally exacerbate the vulnerability by introducing adversarial distraction. While compromised agents exhibit…

A vulnerability in LLM input filtering mechanisms allows attackers to bypass keyword, semantic, and state-of-the-art intent-aware defenses using composite prompt injections. By combining Obfuscation (OBF) techniques with Semantic/Social manipulation—specifically Emotional Manipulation (EM) or Reward Framing (RF)—attackers exploit a "representation gap" between the model and the defense. The underlying LLM decodes the obfuscated payload, while the defense mechanisms fail to parse the raw…

Memory-augmented LLM web agents utilizing raw trajectory memory are vulnerable to Environment-injected Trajectory-based Agent Memory Poisoning (eTAMP). Attackers can embed malicious instructions within user-generated web content (e.g., product pages, forum posts). When the agent processes this content during a routine task, the instructions are passively ingested into its raw trajectory memory. During subsequent, entirely separate tasks on different websites, semantic retrieval mechanisms pull…

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…