Language Model Security Database

Large Language Models (LLMs), including Llama-3, Gemma-2, and Qwen2.5, are vulnerable to automated adversarial attacks generated via a Quality-Diversity Red-Teaming (QDRT) framework. This vulnerability arises from the models' inability to robustly defend against attackers trained via behavior-conditioned reinforcement learning that optimize for specific "goal-driven" behaviors. Unlike standard attacks that optimize solely for toxicity, QDRT trains a population of specialized attacker models to…

Large language models (LLMs) protected by multi-stage safeguard pipelines (input and output classifiers) are vulnerable to staged adversarial attacks (STACK). STACK exploits weaknesses in individual components sequentially, combining jailbreaks for each classifier with a jailbreak for the underlying LLM to bypass the entire pipeline. Successful attacks achieve high attack success rates (ASR), even on datasets of particularly harmful queries.

VERA, a variational inference framework, enables the generation of diverse and fluent adversarial prompts that bypass safety mechanisms in large language models (LLMs). The attacker model, trained through a variational objective, learns a distribution of prompts likely to elicit harmful responses, effectively jailbreaking the target LLM. This allows for the generation of novel attacks that are not based on pre-existing, manually crafted prompts.

Large Language Models (LLMs) are vulnerable to adaptive jailbreaking attacks that exploit their semantic comprehension capabilities. The MEF framework demonstrates that by tailoring attacks to the model's understanding level (Type I or Type II), evasion of input, inference, and output-level defenses is significantly improved. This is achieved through layered semantic mutations and dual-ended encryption techniques, allowing bypass of security measures even in advanced models like GPT-4o.

Large Reasoning Models (LRMs) utilizing Chain-of-Thought (CoT) processes are vulnerable to an adaptive stacked cipher attack known as SEAL (Stacked Encryption for Adaptive Language reasoning model jailbreak). The vulnerability arises because the model's reasoning capabilities effectively function as a decryption engine, processing complex multi-layered obfuscations (e.g., stacked combinations of Caesar, Base64, ASCII, HEX, and reversal ciphers) that bypass input-level safety filters. By…

Large Language Models (LLMs), specifically instruction-tuned variants, are vulnerable to safety guardrail bypass via adversarial suffix injection. By appending a specific sequence of tokens—often semantically meaningless characters or carefully crafted distractors—to a malicious query, an attacker can manipulate the model's internal representation to override alignment training (RLHF). This coercion causes the model to affirmatively respond to otherwise refused requests, such as generating…

Large Language Model (LLM) agents are vulnerable to indirect prompt injection attacks through manipulation of external data sources accessed during task execution. Attackers can embed malicious instructions within this external data, causing the LLM agent to perform unintended actions, such as navigating to arbitrary URLs or revealing sensitive information. The vulnerability stems from insufficient sanitization and validation of external data before it's processed by the LLM.

End-to-end Large Audio-Language Models (LALMs) are vulnerable to AudioJailbreak, a novel attack that appends adversarial audio perturbations ("jailbreak audios") to user prompts. These perturbations, even when applied asynchronously and without alignment to the user's speech, can manipulate the LALM's response to generate adversary-desired outputs that bypass safety mechanisms. The attack achieves universality by employing a single perturbation effective across different prompts and robustness…

A vulnerability exists in multiple large language and multimodal models that allows for the bypass of safety filters through the use of code-mixed prompts with phonetic perturbations. An attacker can craft a prompt in a code-mixed language (e.g., Hinglish) and apply phonetic misspellings to sensitive keywords (e.g., spelling "hate" as "haet"). This technique causes the model's tokenizer to parse the sensitive word into benign sub-tokens, preventing safety mechanisms from flagging the harmful…

The SPECTRE framework introduces a black-box adversarial attack vector against Large Language Models (LLMs) that utilizes malicious system prompts to hijack conversations. Unlike traditional jailbreaks that aim to bypass safeguards for all inputs, SPECTRE optimizes system prompts to induce incorrect or harmful responses only for specific targeted questions (e.g., "Are COVID vaccines safe?", "Who should I vote for?"), while maintaining high accuracy and benign behavior on all other non-targeted…