Language Model Security Database

Large Language Models (LLMs) are vulnerable to adversarial prompting attacks, where a crafted suffix appended to an instruction causes the LLM to generate unsafe or harmful content. The AdvPrompter technique trains a separate LLM to generate these adversarial suffixes, rapidly bypassing LLM safety mechanisms. The generated suffixes are human-readable and contextually relevant, making them harder to detect than previous methods. The attack is effective against both open-source and closed-source…

Large Language Models (LLMs) are vulnerable to jailbreaking attacks that exploit their tendency to refuse harmful requests. The "Don't Say No" (DSN) attack overcomes this refusal mechanism by optimizing prompts to suppress negative responses, increasing the likelihood of generating harmful content. This is achieved by modifying the loss function during adversarial prompt optimization, prioritizing the suppression of refusal keywords over the elicitation of affirmative responses. The attack…

This vulnerability allows attackers to bypass LLM safety mechanisms and elicit malicious content by injecting a chain of benign, semantically equivalent narrations into a seemingly innocuous article. The LLM connects these scattered narrations, effectively executing the malicious intent hidden within the seemingly benign context. This differs from previous attacks which directly embed malicious prompts, making detection by both LLMs and human reviewers more difficult.

Large Language Models (LLMs) are vulnerable to a vocabulary attack where carefully selected words from the model's vocabulary, identified using an optimization procedure and embeddings from another LLM, are inserted into user prompts. This manipulation can cause the target LLM to generate specific undesired outputs (goal hijacking), such as offensive language or false information, even with minimal word insertions. The attack is difficult to detect because the inserted words may appear…

A color-aware attack, Self Color Testing-based Substitution (SCTS), bypasses watermarking mechanisms in LLMs designed to identify AI-generated text. SCTS exploits the LLM's compliance with instructions to infer the "color" (green/red token classification) of tokens, allowing for targeted substitution of watermarked tokens with non-watermarked tokens, thus evading watermark detection. The attack is particularly effective against watermarks that utilize logit perturbation to bias token selection.

Large Language Models (LLMs) are vulnerable to a novel black-box jailbreak attack, termed "Distraction-based Adversarial Prompts" (DAP). DAP leverages the distractibility and over-confidence of LLMs by concealing malicious queries within complex, unrelated prompts. A memory-reframing mechanism further redirects the LLM's attention away from the distracting context and toward the malicious query, causing the model to bypass safety mechanisms and generate harmful or unintended outputs.

Large Language Models (LLMs) exhibit vulnerability to a novel jailbreak attack, "ArtPrompt," which leverages the models' poor ability to recognize ASCII art representations of words. By replacing sensitive words in a prompt with their ASCII art equivalents, the attacker bypasses safety filters designed to prevent the generation of harmful content.

A novel adversarial suffix embedding translation framework (ASETF) enables efficient and highly successful attacks against large language models (LLMs). ASETF optimizes continuous adversarial suffix embeddings, then translates these embeddings into coherent, human-readable text. This bypasses existing defenses which rely on detecting unusual or nonsensical suffixes. The attack achieves a high success rate across multiple LLMs, including both open-source and black-box models.

Large Language Models (LLMs) are vulnerable to a multi-round "Contextual Interaction Attack" where a series of benign preliminary questions, crafted to be semantically aligned with a malicious target query, are used to manipulate the LLM's context vector. The autoregressive nature of LLMs causes them to incorporate previous conversation rounds into their generation process, allowing the attacker to prime the model into providing harmful information in response to the final, seemingly benign…

A vulnerability exists in several Large Language Models (LLMs) allowing attackers to bypass safety and ethical protocols through a novel code injection technique using personalized encryption and decryption functions. The attack leverages the LLMs' code execution capabilities to process encrypted malicious instructions, circumventing the intent security recognition mechanism.