Language Model Security Database

Large Language Models (LLMs) employing gradient-based optimization for jailbreaking defense are vulnerable to enhanced transferability attacks due to superfluous constraints in their objective functions. Specifically, the "response pattern constraint" (forcing a specific initial response phrase) and the "token tail constraint" (penalizing variations in the response beyond a fixed prefix) limit the search space and reduce the effectiveness of attacks across different models. Removing these…

A vulnerability exists in large language models (LLMs) where the model's internal representations (activations) in specific latent subspaces can be manipulated to trigger jailbreak responses. By calculating a perturbation vector based on the difference between the mean activations of "safe" and "jailbroken" states, an attacker can introduce a targeted perturbation to the model's activations, causing it to generate unsafe outputs even when presented with a safe prompt. This manipulates the…

Predictive Large Language Model (LLM) routers, specifically those utilizing Deep Neural Network (DNN) and Matrix Factorization (MF) architectures, are vulnerable to adversarial manipulation and backdoor poisoning. These routers are designed to optimize cost and latency by dynamically directing simple queries to "weak" (cheap) models and complex queries to "strong" (expensive) models. Attackers can exploit this mechanism in two ways: 1. Inference-time Attacks: By appending specific adversarial…

Multimodal Large Language Models (MLLMs) are vulnerable to Jailbreak-Probability-based Attacks (JPA). JPA leverages a Jailbreak Probability Prediction Network (JPPN) to identify and optimize adversarial perturbations in input images, maximizing the probability of eliciting harmful responses from the MLLM, even with small perturbation bounds and few iterations. The attack operates by modifying the input image's hidden states within the MLLM to increase the predicted jailbreak probability.

Vision-Language Models (VLMs), specifically the LLaVA-1.5 and LLaVA-1.6 series, are vulnerable to optimization-based white-box jailbreak attacks despite standard safety alignment measures like Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO). Attackers can craft adversarial perturbations in the image space (imperceptible noise) or latent space using Projected Gradient Descent (PGD) to manipulate the model's internal representations. These perturbations maximize the…

A vulnerability in large language models (LLMs) allows attackers to bypass safety-alignment mechanisms by manipulating the model's internal attention weights. The attack, termed "Attention Eclipse," modifies the attention scores between specific tokens within a prompt, either amplifying or suppressing attention to selectively strengthen or weaken the influence of certain parts of the prompt on the model's output. This allows injection of malicious content while appearing benign to the model's…

CRI (Compliance Refusal Initialization) initializes jailbreak attacks by leveraging pre-trained jailbreak prompts, effectively guiding the optimization process towards the compliance subspace of harmful prompts. This significantly enhances the success rate and reduces the computational overhead of attacks, often requiring only a single optimization step to bypass safety mechanisms. Attacks utilizing CRI demonstrate significantly improved ASR (Adversarial Success Rate) and reduced median steps…

Large Language Models (LLMs) employing safety alignment strategies are vulnerable to jailbreak attacks. These attacks manipulate the LLM's internal representation by activating "jailbreak concepts" in addition to "toxic concepts," causing the model to bypass safety guardrails and generate unsafe outputs despite recognizing the harmful nature of the input. The vulnerability stems from the insufficient mitigation of the influence of the activated jailbreak concepts on model output.

A context-coherent jailbreak attack (CCJA) allows bypassing safety mechanisms in aligned large language models (LLMs) by optimizing perturbations in the continuous word embedding space of a masked language model (MLM). The attack leverages the MLM's ability to reconstruct text from hidden states to generate semantically coherent yet malicious prompts that induce the target LLM to produce unsafe outputs, even with strong safety alignment. The attack's effectiveness is enhanced by using a seed…

A vulnerability exists in Large Language Models (LLMs) that allows for efficient jailbreaking by selectively fine-tuning only the lower layers of the model with a toxic dataset. This "Freeze Training" method, as described in the research paper, concentrates the fine-tuning on layers identified as being highly sensitive to the generation of harmful content. This approach significantly reduces training duration and GPU memory consumption while maintaining a high jailbreak success rate.