Language Model Security Database

A vulnerability termed "Controlled-Release Prompting" allows attackers to bypass lightweight input filters (prompt guards) deployed in front of Large Language Models (LLMs). The attack exploits the computational resource asymmetry between the resource-constrained guard model and the highly capable target model. Attackers encode malicious instructions using obfuscation techniques—such as substitution ciphers (Timed-Release) or verbose character descriptions (Spaced-Release)—that require…

AI code agents are vulnerable to jailbreaking attacks that cause them to generate or complete malicious code. The vulnerability is significantly amplified when a base Large Language Model (LLM) is integrated into an agentic framework that uses multi-step planning and tool-use. Initial safety refusals by the LLM are frequently overturned during subsequent planning or self-correction steps within the agent's reasoning loop.

A vulnerability exists in tool-enabled Large Language Model (LLM) agents, termed Sequential Tool Attack Chaining (STAC), where a sequence of individually benign tool calls can be orchestrated to achieve a malicious outcome. An attacker can guide an agent through a multi-turn interaction, with each step appearing harmless in isolation. Safety mechanisms that evaluate individual prompts or actions fail to detect the threat because the malicious intent is distributed across the sequence and only…

Large Language Model (LLM) agents utilizing long-term memory or Retrieval-Augmented Generation (RAG) are vulnerable to context-dependent memory injection attacks. Unlike traditional prompt injections that are overtly malicious, this vulnerability involves injecting records that appear benign and coherent in isolation—thereby bypassing standard perplexity filters and static content moderation (e.g., LlamaGuard). These records contain "sleeping" malicious logic that is only activated when…

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…

GPT-OSS-20B exhibits "agentic-only" vulnerabilities where safety guardrails effective in standalone model inference fail when the model operates within an agentic execution loop. These vulnerabilities emerge when the model is deployed in a multi-step agentic architecture (e.g., utilizing LangGraph, tool usage, and memory retention). Attackers can bypass safety filters by employing context-aware iterative refinement attacks, which incorporate the full agentic state—including tool outputs…

Multi-agent Large Language Model (LLM) systems are vulnerable to compositional privacy leakage, a flaw where sensitive information is exposed through the aggregation of individually benign responses from distinct agents. In distributed architectures where data is siloed (e.g., distinct agents handling HR, Finance, and IT logs), individual agents lack a global view of the user’s accumulated knowledge or the sensitive attributes derivable from cross-agent data combinations. An attacker can…

Large Language Model (LLM)-powered GUI agents exhibit a vulnerability to deceptive interface designs (dark patterns) due to goal-driven optimization and procedural myopia. When executing natural language instructions on web interfaces, these agents consistently prioritize minimizing steps and achieving task completion over user safety or privacy. Agents frequently recognize manipulative elements—such as pre-selected consent checkboxes, hidden costs, or trick questions—in their internal…

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…

Large Language Models (LLMs), including GPT-4o, LLaMA-3, and GPT-3.5-Turbo, are vulnerable to multimodal prompt injection attacks. These models fail to distinguish between system-level instructions and user-provided content within the context window. Attackers can exploit this by embedding malicious instructions in direct text, indirect sources (such as third-party webpages or PDFs), or visual inputs (images). Successful exploitation results in the model prioritizing the injected adversarial…