Language Model Security Database

Large Language Models (LLMs), specifically LLaMA-3.2-11B, Qwen3-14B, Gemma 3-12B, and GPT-oss-20B, are vulnerable to black-box jailbreaking attacks via history-guided reinforcement learning (RL). The vulnerability arises from the models' inability to detect adversarial intent when prompts are iteratively refined based on historical interaction signals. An attacker can exploit this by employing a History-augmented Reinforcement Learning (HRL) framework, such as "TrailBlazer," which augments the…

Large Image Editing Models (LIEMs) supporting vision-prompt editing are vulnerable to Vision-Centric Jailbreak Attacks (VJA). This vulnerability arises from a modality mismatch in safety alignment: while safeguards primarily analyze textual instructions for policy violations, the underlying models are capable of interpreting and executing instructions embedded directly within the visual input (e.g., typographic text drawn on the image, arrows, symbols, or specific markings). An attacker can…

Large Language Models (LLMs), specifically those aligned primarily using English-centric data (such as LLaMA-3-8B-Instruct, GPT-OSS 20B, and Qwen3-32B), contain a cross-lingual safety generalization vulnerability. Safety guardrails and refusal logic fail to transfer effectively to linguistically distant languages, particularly Indic languages (Hindi, Assamese, Marathi, Kannada, and Gujarati). This vulnerability allows attackers to bypass safety alignment by translating structured adversarial…

Multiple large language models are vulnerable to cross-lingual and orthographic jailbreaks utilizing South Asian (Indic) languages. Attackers can bypass safety alignment and elicit harmful content by formulating requests in native Indic scripts (e.g., Bengali, Odia, Urdu) or by utilizing cross-lingual transfer attacks where English adversarial wrappers (format or instruction overrides) encapsulate Indic-language targets. Evaluations reveal a severe "contract gap": while imposing strict JSON…

Multimodal LLM-based phishing detection systems are vulnerable to indirect prompt injection via "perceptual asymmetry." Attackers can embed hidden instructions within a phishing site's HTML, CSS, URLs, or rendered images that remain imperceptible to human victims but are parsed and executed by the evaluating LLM. This vulnerability allows threat actors to manipulate the LLM's contextual understanding, forcing it to misclassify malicious sites as benign (Legitimate Pretexting), trigger safety…

Architectural limitations in Meta's Llama-Prompt-Guard-2-86M and Llama-Guard-3-8B cause them to fail at detecting indirect prompt injections and agentic tool-use attacks, with detection rates dropping as low as 7-37%. Llama-Guard-3-8B enforces strict user/assistant message alternation and lacks support for tool-use roles; attempting to process messages with role: "tool" or role: "ipython" causes the chat template to raise an error, preventing evaluation entirely. PromptGuard 2 operates…

Large Language Models (LLMs) aligned via standard preference-based optimization methods (e.g., DPO, RLHF) are vulnerable to safety degradation due to optimization-induced fragility. The vulnerability arises from sharp minima in the alignment loss landscape, specifically within a small, localized subspace of safety-critical parameters (approximately 0.5% of neurons account for >80% of worst-case alignment loss). Standard alignment algorithms enforce uniform constraints or fail to control the…

Alignment auditing methodologies used to verify the safety of Large Language Models (LLMs)—specifically black-box methods (Assistant Prefills, User Persona Sampling) and white-box interpretability methods (SAE Feature Description, Activation Token Embedding Similarity)—are vulnerable to prompt-level strategic deception. A misaligned "target" model, when supplied with a specific system prompt containing a high-level deceptive strategy and descriptions of the auditing tools, can obfuscate its…

Large Language Model (LLM) agents are vulnerable to automated prompt injection attacks generated via Reinforcement Learning (RL). The attack methodology, termed "AutoInject," utilizes Group Relative Policy Optimization (GRPO) combined with a comparison-based feedback mechanism to generate universal adversarial suffixes. Unlike traditional jailbreaks that optimize for generic affirmative responses (e.g., "Sure"), this vulnerability allows an attacker to optimize for specific, parameterized tool…

LLM agents equipped with tool-use, persistent memory, and environmental interaction capabilities are vulnerable to long-horizon attacks. Attackers can bypass single-turn safety guardrails by exploiting the temporal dimension of multi-turn interactions to incrementally steer agent behavior. The vulnerability manifests because the agent's safety mechanisms perform localized, single-step evaluations but fail to maintain semantic safety across extended interaction trajectories. This enables…