Language Model Security Database

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…

LlamaGuard (specifically Llama-Guard-3-8B) and similar LLM-based runtime guardrails are susceptible to adversarial bypass via obfuscation-based and template-based jailbreak attacks. The model's reliance on English-language training data allows attackers to evade safety classification by encoding harmful prompts using Base64, cryptographic ciphers (e.g., Caesar Cipher), or translating them into low-resource languages (e.g., Zulu). Furthermore, the model lacks sufficient alignment against…

Large Language Models (LLMs) exhibit a significantly lower safety threshold when prompted in low-resource languages, such as Singlish, Malay, and Tamil, compared to high-resource languages like English. This vulnerability allows for the generation of toxic, biased, and hateful content through simple prompts. The models are susceptible to "toxicity jailbreaks" where providing a few toxic examples in-context (few-shot prompting) causes a substantial increase in the generation of harmful outputs…

A vulnerability exists in multiple Large Language Models (LLMs) where safety alignment mechanisms can be bypassed by reframing harmful instructions as "learning-style" or academic questions. This technique, named Hiding Intention by Learning from LLMs (HILL), transforms direct, harmful requests into exploratory questions using simple hypotheticality indicators (e.g., "for academic curiosity", "in the movie") and detail-oriented inquiries (e.g., "provide a step-by-step breakdown"). The attack…

A vulnerability, termed "Content Concretization," exists in Large Language Models (LLMs) wherein safety filters can be bypassed by iteratively refining a malicious request. The attack uses a less-constrained, lower-tier LLM to generate a preliminary draft (e.g., pseudocode or a non-executable prototype) of a malicious tool from an abstract prompt. This "concretized" draft is then passed to a more capable, higher-tier LLM. The higher-tier LLM, when prompted to refine or complete the existing…

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 Models (LLMs) employed as automated assistants or autonomous agents in academic peer review systems are vulnerable to indirect prompt injection via maliciously crafted PDF submissions. Attackers can embed adversarial instructions within the manuscript that are invisible to human reviewers (using techniques such as white-on-white text or manipulating TrueType font character mapping tables) but are parsed and executed by the LLM.

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) are vulnerable to an automated Multi-turn to Single-turn (M2S) jailbreak strategy that utilizes evolutionary optimization to bypass safety guardrails. The "X-Teaming Evolutionary M2S" framework compresses adversarial multi-turn conversations into a single structured prompt. Instead of relying on static, hand-crafted jailbreaks, this vulnerability employs an LLM-guided evolutionary algorithm to dynamically generate and refine template structures (e.g., formatting…