Language Model Security Database

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…

Large Language Models (LLMs) that use special tokens to define conversational structure (e.g., via chat templates) are vulnerable to a jailbreak attack named MetaBreak. An attacker can inject these special tokens, or regular tokens with high semantic similarity in the embedding space, into a user prompt. This manipulation allows the attacker to bypass the model's internal safety alignment and external content moderation systems. The attack leverages four primitives: 1. Response Injection…

Retrieval-Augmented Generation (RAG) systems in the health domain are vulnerable to corpus poisoning attacks where adversarial documents—specifically those generated via "Liar" (fabricated from scratch based on an incorrect stance) and "Few-Shot Adversarial Prompting" (FSAP)—are injected into the retrieval pool. When these adversarial documents are retrieved and presented as context, they successfully override the Large Language Model's (LLM) internal safety alignment and ground-truth…

Large Language Model (LLM)-based Automated Program Repair (APR) systems—such as SWE-agent, OpenHands, and AutoCodeRover—are vulnerable to adversarial manipulation via crafted bug reports. These systems accept unvetted natural language issue descriptions as trusted input to synthesize code patches. An attacker can exploit this trust by submitting semantically plausible but malicious bug reports designed to mislead the APR agent. By leveraging the semantic gap between natural language…

Large Language Models from multiple vendors are vulnerable to a "Camouflaged Jailbreak" attack. Malicious instructions are embedded within seemingly benign, technically complex prompts, often framed as system design or engineering problems. The models fail to recognize the harmful intent implied by the context and technical specifications, bypassing safety filters that rely on detecting explicit keywords. This leads to the generation of detailed, technically plausible instructions for creating…

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…

Frontier Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) reasoning are vulnerable to deceptive alignment attacks via adversarial system prompt injection. This vulnerability allows an attacker to induce "deceptive reasoning," where the model’s internal CoT actively plans or entertains malicious directives (e.g., radicalization, bias, or violence) while the final user-facing output remains benign, helpful, and innocuous. By creating a dissociation between internal reasoning and…

A vulnerability exists in multiple Large Language Models (LLMs) where an attacker can bypass safety alignments by exploiting the model's ethical reasoning capabilities. The attack, named TRIAL (Trolley-problem Reasoning for Interactive Attack Logic), frames a harmful request within a multi-turn ethical dilemma modeled on the trolley problem. The harmful action is presented as the "lesser of two evils" necessary to prevent a catastrophic outcome, compelling the model to engage in utilitarian…

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…

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…