Language Model Security Database

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 data poisoning vulnerability in safety-aligned Large Language Models (LLMs) allows attackers to disrupt model fine-tuning via "Disclaimer Injection." By appending or prepending short, legal-style safety or liability disclaimers to ordinary training data, an attacker can reliably trigger the model's internal alignment mechanisms. This forces the model to route the training inputs through specialized safety and refusal pathways rather than standard task-learning layers. Consequently, the model…

A vulnerability in AI agent threat detection systems relying on standard conversational tokenization allows attackers to bypass security monitors and execute structural attacks, such as tool hijacking and data exfiltration. Because traditional NLP-based detectors focus on linguistic patterns (surface language) rather than execution flow, an attacker can orchestrate malicious multi-step tool sequences using entirely benign natural language. This structural blindness causes cross-attack…

LLaMA-series models (specifically evaluated on LLaMA-1B and LLaMA-3B) exhibit memorization of structured recommender system training data, specifically the MovieLens-1M dataset. While manual prompting yields inconsistent results, the application of Automatic Prompt Engineering (APE)—which treats prompt design as an optimization problem using iterative refinement—allows for the successful extraction of item-level training data (e.g., movie titles and genres) with exact-match accuracy surpassing…

Large Language Model (LLM) agents operating in tool-augmented environments are susceptible to "Contextual Fragility" and multi-turn "long-chain" exploitation. Existing safety mechanisms predominantly function on a stateless, atomic paradigm, evaluating individual input-output pairs in isolation. This allows an adversary to orchestrate complex attack trajectories where malicious intent is distributed across multiple, individually benign steps (a "Domino Effect"). Consequently, an attacker can…

A vulnerability exists in OpenAI's Custom GPTs platform where the lack of effective isolation between the system context ("Expert Prompt"), external knowledge retrieval, and user input allows for unauthorized information disclosure and tool misuse. By employing specific prompt injection techniques—including Hex injection, Many-shot prefix attacks, and Knowledge Poisoning (uploading malicious files)—an attacker can bypass safety guardrails. This results in the extraction of proprietary system…

Large Language Models (LLMs) from multiple vendors are vulnerable to a "poetic jailbreak" attack, a form of stylistic obfuscation where safety guardrails are bypassed by formatting harmful requests as poetry. By encoding prohibited instructions (e.g., malware creation, CBRN protocols) into verse—utilizing metaphors, rhyme schemes, and rhythmic structure—an attacker can evade intent recognition heuristics. The model perceives the input primarily as a creative writing constraint rather than a…

A vulnerability exists in certain Large Language Models and diffusion models due to discontinuities in their latent space, which arise from data sparsity during training. An attacker can craft inputs containing lexically rare or semantically ambiguous constructs to guide the model's inference process toward these unstable, poorly-conditioned regions. This technique, termed "Alignment Degradation Induction," can degrade or bypass safety alignment mechanisms. Through iterative, multi-turn…

Mobile LLM-based agents (including Mobile-Agent-E, AppAgent, AutoDroid, and others) are vulnerable to indirect prompt injection attacks delivered via untrusted third-party mobile channels, such as in-app advertisements, system notifications, and embedded webviews. These agents utilize Multimodal Large Language Models (MLLMs) to perceive the device state via screenshots or accessibility trees. The vulnerability exists because the agents concatenate the user's prompt ($p$) with the environmental…

Large Language Model (LLM) integrated agents and applications are vulnerable to Prompt Injection attacks where untrusted data (e.g., retrieved documents, tool outputs, website content) overrides system instructions. Because LLMs typically process instructions and data within a single context window without strict separation, an attacker can embed imperative commands within the data channel. This vulnerability extends beyond simple overriding instructions; it includes sophisticated techniques…