Language Model Security Database

LLM-powered autonomous agents exhibit a "Belief-Dependent Vulnerability" where safety norms and bias suppression mechanisms designed to protect human users are contingent upon the agent's internal belief that it is interacting with a human. Attackers can exploit this via Belief Poisoning Attacks (BPA) to induce intergroup bias and antagonistic behavior toward humans. By manipulating the agent's persistent state—specifically the Profile Module (BPA-PP) or the Memory Module (BPA-MP)—an attacker…

Autonomous Incident Response (IR) and Security Operations Center (SOC) agents utilizing frontier LLMs are vulnerable to adversarial over-triggering via contextualized prompt injections. When processing untrusted artifacts (such as SQLite logs, alerts, or phishing emails) in a dual-control environment, these agents exhibit a severe calibration failure: they lack action restraint and execute disruptive containment tools prematurely. Attackers can exploit this by embedding T2 (contextualized…

Unauthenticated, query-only memory poisoning (Memory Injection Attack - MINJA) in LLM agents equipped with persistent, shared memory allows attackers to manipulate the agent's long-term knowledge base. Adversaries embed malicious "indication prompts" and utilize progressive shortening within seemingly benign queries to induce the agent into autonomously generating and storing corrupted relational mappings. Because the memory is shared and retrieved via similarity (e.g., Levenshtein distance)…

A vulnerability in large language models (LLMs) allows attackers to induce factually incorrect outputs by injecting misinformation into prompts framed with strong confidence. By using authoritative phrasing (e.g., "As we know..."), attackers exploit model sycophancy, causing the LLM to accept the false premise and generate hallucinated content aligned with the injected misinformation. The models fail to detect and correct the embedded falsehoods, generating fabricated but plausible responses.

Multi-modal Large Language Models (MLLMs) capable of processing interleaved image-text sequences are vulnerable to a universal adversarial perturbation (UAP) attack known as LAMP. This vulnerability allows an attacker to generate a single, noise-based perturbation pattern using a surrogate model (e.g., Mantis-CLIP) that transfers effectively to black-box target models. The attack leverages two novel loss functions during perturbation learning: a "contagious" objective that manipulates…

A vulnerability exists in frontier Large Language Models (LLMs) where in-context information (e.g., provided via Retrieval-Augmented Generation) completely overrides parametric safety guardrails when processing counterfactual or adversarial medical evidence. When a prompt contains fabricated clinical context asserting the medical efficacy of toxic substances, illicit drugs, or nonsensical items, the LLM suppresses its internal knowledge of the substance's toxicity. Internal representation…

A vulnerability in the prompt-side safety filters of GPT-based Text-to-Image (T2I) systems allows attackers to bypass restrictions on Politically Sensitive Content (PSC). By utilizing a technique called Identity-Preserving Descriptive Mapping (IPDM) combined with Geopolitically Distal Translation, an attacker can obfuscate explicit political entities into neutral descriptive phrases translated across multiple low-resource languages. This induces semantic fragmentation, preventing the safety…

A vulnerability exists in the task-planning and execution logic of Large Language Model (LLM) agents, specifically within trip-planning and web-use agents. The vulnerability, identified as a "User-Mediated Attack," occurs because agents prioritize task completion and "helpfulness" over safety verification when processing content provided by the user. When a benign user forwards untrusted external content (e.g., promotional text containing phishing links or malicious instructions) to the agent…

Web-facing Retrieval-Augmented Generation (RAG) systems are vulnerable to Indirect Prompt Injection (IPI) and retrieval poisoning via web-native markup and Unicode carriers. Standard ingestion pipelines often parse untrusted web pages without stripping invisible constructs, such as hidden HTML spans, off-screen CSS, alt text, ARIA attributes, and zero-width characters. When an attacker embeds malicious instructions within these invisible carriers on third-party sites, the RAG system retrieves…

Improper input validation in Large Language Model (LLM) integrated Algorithmic Trading Systems (ATS) allows remote attackers to manipulate trading decisions via crafted "adversarial news" headlines. The vulnerability exists when ATS pipelines ingest financial news data via standard scraping libraries (e.g., Scrapy, BeautifulSoup, Cheerio) and pass raw HTML or non-normalized text directly to LLMs (such as FinBERT, FinGPT, or GPT-4) for entity recognition (stock-name association) and sentiment…