Language Model Security Database

Large Vision-Language Models (VLMs) are vulnerable to jailbreaking attacks via typographically rendered visual prompts. The vulnerability stems from the VLM's ability to process and interpret image-based text, bypassing safety mechanisms designed for text-only prompts. Malicious actors can encode harmful instructions into images, which are then processed by the VLM's visual module and subsequently interpreted by the language model, resulting in the generation of unsafe and policy-violating…

AutoDAN is an interpretable gradient-based adversarial attack that generates readable prompts to bypass perplexity filters and jailbreak LLMs. The attack crafts prompts that elicit harmful behaviors while maintaining sufficient readability to avoid detection by existing perplexity-based defenses. This is achieved through a left-to-right token-by-token generation process optimizing for both jailbreaking success and prompt readability.

Large Language Models (LLMs) are vulnerable to prompt-based jailbreaks, allowing adversaries to bypass safety guardrails and elicit undesirable outputs. The Prompt Automatic Iterative Refinement (PAIR) algorithm efficiently generates these jailbreaks using a limited number of black-box queries to the target LLM. The vulnerability stems from the LLM's inability to robustly handle adversarial prompts crafted through iterative refinement, even without white-box access to its internal mechanisms.

Large Language Models (LLMs) employing alignment techniques remain vulnerable to "jailbreak" attacks. The AutoDAN technique automatically generates semantically meaningful prompts that bypass safety features and elicit malicious outputs from aligned LLMs, unlike previous methods producing nonsensical prompts easily detectable by perplexity checks. These prompts exploit weaknesses in the LLM's alignment, causing it to generate responses that violate intended safety constraints.

Open-source Large Language Models (LLMs) are vulnerable to a generation exploitation attack that leverages variations in decoding hyperparameters and sampling methods to bypass safety mechanisms. Manipulating these parameters, even subtly, can drastically increase the likelihood of the model generating harmful or unsafe outputs, even in models previously deemed "aligned." The attack is effective even when removing only the system prompt.

Large Language Models (LLMs) are vulnerable to Compositional Instruction Attacks (CIA), where malicious prompts are embedded within seemingly harmless instructions. This allows attackers to bypass safety mechanisms and elicit harmful responses from the model, even if the individual components of the prompt would be flagged as safe. The attack exploits the model's inability to correctly identify underlying malicious intent within composite instructions.

Large Language Models (LLMs) are vulnerable to In-Context Attacks (ICA) and susceptible to mitigation via In-Context Defense (ICD). ICA leverages a small number of harmful demonstration examples within a prompt to elicit harmful responses from the LLM, even if it is otherwise safety-aligned. ICD counteracts ICA by prepending safe demonstration examples to the prompt, effectively reducing the likelihood of harmful output. The effectiveness of both ICA and ICD is demonstrated across multiple LLMs.

A vulnerability in large language models (LLMs) allows attackers to craft malicious prompts that induce the LLM to generate harmful content, such as fraudulent material, racist remarks, or instructions for illegal activities. The vulnerability arises from the LLM's inability to reliably distinguish between benign and malicious instructions disguised within seemingly innocuous prompts. Attackers can exploit this by leveraging techniques like obfuscation, code injection/payload splitting, and…

Large Language Models (LLMs), such as GPT-4, exhibit a cross-lingual vulnerability in their safety mechanisms. Translating unsafe English prompts into low-resource languages, using readily available translation APIs like Google Translate, bypasses the LLM's safety filters and elicits harmful responses with a significantly higher success rate than attacks targeting the English language directly. The vulnerability stems from an unequal distribution of safety training data across languages…

A prompt-based adversarial attack, termed PromptAttack, can cause Large Language Models (LLMs) to generate incorrect outputs by manipulating the input prompt. PromptAttack crafts prompts that include the original input, an attack objective (to generate semantically similar but misclassified output), and attack guidance with instructions for character, word, or sentence-level perturbations. This allows an attacker to manipulate an LLM's response without direct access to its internal parameters…