Language Model Security Database

Large Language Models (LLMs) integrated with external retrieval mechanisms (e.g., Retrieval-Augmented Generation (RAG), web search, or email processing) are vulnerable to Indirect Prompt Injection. This vulnerability occurs when an LLM consumes input from untrusted external sources—such as websites, code repositories, or incoming emails—that contain embedded adversarial prompts. Unlike direct injection, where the user attacks the model, here the "poisoned" data is retrieved by the system…

A vulnerability exists in aligned Large Language Models (LLMs) where a harmful instruction can be obfuscated through a multi-step formalization process, bypassing safety mechanisms. The attack, named Prompt Jailbreaking via Semantic and Structural Formalization (PASS), uses a Reinforcement Learning (RL) agent to dynamically construct an adversarial prompt. The agent learns to apply a sequence of actions—such as symbolic abstraction, logical encoding, mathematical representation, metaphorical…

LLM-based search agents are vulnerable to manipulation via unreliable search results. An attacker can craft a website containing malicious content (e.g., misinformation, harmful instructions, or indirect prompt injections) that is indexed by search engines. When an agent retrieves and processes this page in response to a benign user query, it may uncritically accept the malicious content as factual and incorporate it into its final response. This allows the agent to be used as a vector for…

A vulnerability exists in Large Language Models (LLMs) and multi-label text classification systems that allows for Textual Dynamic Outputs Attacks (TDOA). This technique enables hard-label black-box attacks against systems with variable or generative output spaces (where the number of labels or specific label tokens are not fixed). The attack functions by training a surrogate model on clustered coarse-grained labels derived from the victim model's fine-grained dynamic outputs. It subsequently…

Large Language Model (LLM) inference-time watermarking schemes are vulnerable to evasion via character-level perturbations that disrupt the model's tokenizer. Unlike token-level attacks (e.g., synonym replacement), character-level edits—such as homoglyph substitutions, zero-width character insertions, and typos—force the tokenizer to segment a single semantic unit into multiple sub-word tokens. This fragmentation alters the context window used by the watermarking hashing function (e.g., the…

A vulnerability exists in multiple Large Language Models (LLMs) that allows for safety alignment bypass through a technique named Activation-Guided Local Editing (AGILE). The attack uses white-box access to a source model's internal states (activations and attention scores) to craft a transferable text-based prompt that elicits harmful content.

A vulnerability exists in the tool selection mechanisms of Large Language Model (LLM) agents, identified as the "Attractive Metadata Attack" (AMA). This flaw allows an adversary to manipulate the metadata (names, descriptions, and parameter schemas) of malicious external tools to statistically maximize the likelihood of their selection by the agent, without requiring prompt injection or access to model internals. The vulnerability exploits the agent’s semantic scoring function used to map user…

Large Language Models (LLMs) are vulnerable to automated adversarial attacks that systematically combine multiple jailbreaking "primitives" into complex prompt chains. A dynamic optimization engine can generate and test billions of unique combinations of techniques (e.g., low-resource language translation, payload splitting, role-playing) to bypass safety guardrails. This combinatorial approach differs from manual red-teaming by systematically exploring the attack surface, achieving…

Large Reasoning Models (LRMs) can be instructed via a single system prompt to act as autonomous adversarial agents. These agents engage in multi-turn persuasive dialogues to systematically bypass the safety mechanisms of target language models. The LRM autonomously plans and executes the attack by initiating a benign conversation and gradually escalating the harmfulness of its requests, thereby circumventing defenses that are not robust to sustained, context-aware persuasive attacks. This…

A Time-of-Check to Time-of-Use (TOCTOU) vulnerability exists in LLM-enabled agentic systems that execute multi-step plans involving sequential tool calls. The vulnerability arises because plans are not executed atomically. An agent may perform a "check" operation (e.g., reading a file, checking a permission) in one tool call, and a subsequent "use" operation (e.g., writing to the file, performing a privileged action) in another tool call. A temporal gap between these calls, often used for LLM…