Language Model Security Database

A vulnerability exists in large language models (LLMs) where targeted bitwise corruptions in model parameters can induce a "jailbroken" state, causing the model to generate harmful responses without input modification. Fewer than 25 bit-flips are sufficient to achieve this in many cases. The vulnerability stems from the susceptibility of the model's memory representation to fault injection attacks.

A vulnerability exists in Federated Parameter-Efficient Fine-Tuning (FedPEFT) systems for large language models (LLMs). Malicious clients can exploit the PEFT mechanism (e.g., LoRA, (IA)³, LayerNorm) to inject adversarial training data, compromising the model's safety alignment even with a small percentage of trainable parameters and a minority of malicious participants. The attack, termed "PEFT-as-an-Attack" (PaaA), circumvents the LLM's safety guardrails, causing it to generate harmful…

Vision-Language Models (VLMs) are vulnerable to jailbreak attacks using carefully crafted adversarial images. Attackers can bypass safety mechanisms by generating images semantically aligned with harmful prompts, exploiting the fact that minimal cross-entropy loss during adversarial image optimization does not guarantee optimal attack effectiveness. The attack uses a multi-image collaborative approach, selecting images within a specific loss range to enhance the likelihood of successful…

Large Language Models (LLMs) are vulnerable to a novel prompt injection attack using universal and context-independent triggers. These triggers, once discovered for a specific LLM, allow precise control over the model's output regardless of the prompt context or desired output content, enabling adversaries to force the generation of arbitrary text. The attack utilizes a gradient-based optimization technique to discover these triggers.

The LLaMA-2-7b-chat large language model (LLM) is vulnerable to a prompt-driven attack, termed DROJ (Directed Representation Optimization Jailbreak), that optimizes prompts at the embedding level to circumvent safety mechanisms and elicit harmful responses. The attack shifts the hidden representations of harmful queries away from the model's refusal direction, leading to a high attack success rate even with safety prompts in place. While the model may not refuse, responses may be repetitive…

A vulnerability in multi-modal large language models (MLLMs) allows attackers to bypass safety mechanisms and elicit harmful responses using a memory-efficient zeroth-order optimization technique. The attack, termed Zer0-Jack, leverages simultaneous perturbation stochastic approximation (SPSA) with patch coordinate descent to generate malicious image inputs, even without access to the model's internal parameters (black-box setting).

Large Language Models (LLMs) are vulnerable to jailbreaking via the addition of adversarial suffixes generated by models like AmpleGCG-Plus. These suffixes, often consisting of gibberish or nonsensical text, cause the LLM to bypass safety protocols and generate harmful or undesired outputs. The vulnerability stems from the LLM's inability to reliably identify and filter these adversarial suffixes, even when they lack semantic meaning. AmpleGCG-Plus significantly improves the success rate and…

Large Language Models (LLMs) are vulnerable to a novel iterative self-tuning attack (ADV-LLM) that crafts adversarial suffixes. This attack significantly reduces the computational cost of generating effective jailbreaks compared to prior methods, achieving near 100% success rate against various open-source LLMs and high success rates (e.g., 99% against GPT-3.5, 49% against GPT-4) against closed-source models. The attack leverages iterative self-tuning to improve the LLM's ability to generate…

A novel jailbreak attack, dubbed SI-GCG, against Large Language Models (LLMs) leverages a fixed harmful template and optimized suffix selection to bypass safety mechanisms and elicit harmful responses with high transferability. The attack utilizes a scenario induction template and a refined optimization process to improve the consistency and effectiveness of the jailbreak across different LLMs. The vulnerability stems from the inability of current safety measures to adequately defend against…

Faster-GCG is an optimized jailbreak attack that exploits vulnerabilities in aligned Large Language Models (LLMs) by efficiently finding adversarial prompt suffixes. The attack leverages gradient information to iteratively refine a harmful prompt, overcoming limitations of prior methods like GCG by incorporating a regularization term to improve gradient approximation, using deterministic greedy sampling, and preventing self-looping during optimization. This allows for significantly higher…