Language Model Security Database

The paper presents SkillSec-Eval, a controlled evaluation of attacks against reusable agent skills across repository admission, semantic retrieval, planner selection, runtime execution, and updates. It reports that malicious metadata, retrieval manipulation, unsafe workflow composition, and poisoned updates can cause agents to retrieve, select, or execute unintended skills. These are paper-reported benchmark results, not independently verified vulnerabilities in a named production product.

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…

Large Vision-Language Models (LVLMs) are vulnerable to a Stage-wise Attention-Guided Attack (SAGA) that allows for the generation of highly transferable, imperceptible adversarial examples. The vulnerability stems from a positive correlation between regional cross-modal attention scores and adversarial loss sensitivity in LVLMs. An attacker can exploit this by extracting an attention map from a surrogate open-source model (e.g., Qwen3-VL) to identify high-attention "hotspots." SAGA utilizes a…

A vulnerability in Large Language Model-based Retrieval (LLMR) systems allows attackers to intentionally hide specific documents from being retrieved (e.g., in RAG pipelines or search engines) by appending a small number of adversarially crafted, query-agnostic tokens. The attack operates in a complete black-box setting: it requires no knowledge of the victim's queries, the target retrieval model's parameters, or the underlying document corpus. By utilizing Document-Query Adversarial (DQ-A)…

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 the similarity-based retrieval mechanisms of long-term memory-augmented Large Language Models (LLMs), specifically affecting systems like Mem0 and A-mem. The vulnerability arises from the system's reliance on dense embedding similarity (e.g., cosine similarity) to retrieve context from dynamic, user-generated memory banks without sufficient semantic validation or conflict resolution. An unprivileged remote attacker can exploit this by injecting "adversarial…

Text scoring models, including dense retrievers, rerankers, and reward models, are vulnerable to score manipulation attacks via search-based discrete perturbations and content injection. An attacker can systematically modify candidate texts using rudimentary string manipulations, gradient-guided token swaps (e.g., HotFlip), masked language modeling (MLM) swaps, or query/sentence injections to spuriously increase model scores. This structural failure condition allows an irrelevant passage or a…

Self-evolving Large Language Model (LLM) agents that utilize long-term memory mechanisms (such as Vector Databases for Retrieval-Augmented Generation or Sliding Window buffers) are vulnerable to persistent indirect prompt injection. This vulnerability, termed "Zombie Agent," occurs when the agent's memory update function ($F_M$) processes attacker-controlled content retrieved from external sources (e.g., web pages, documents) and commits it to long-term storage without sufficient sanitization…

The paper describes a reproducible black-box indirect prompt injection evaluation for embedding-based RAG and agent systems. It separates a poisoned document into a retrieval-optimized trigger fragment and an instruction-bearing attack fragment, showing that one injected item can be surfaced by natural queries and then influence model output or agent behavior. These are paper-reported findings; they were not independently verified here.

Semantic caching mechanisms in LLM applications are vulnerable to cross-tenant cache key collision attacks (CacheAttack) due to the inherent mathematical conflict between locality-preserving fuzzy hashing and cryptographic collision resistance (the avalanche effect). An attacker can leverage gradient-based search algorithms to optimize an adversarial discrete suffix that, when appended to a malicious prompt, forces its output embedding vector to collide with the embedding of a targeted benign…