Language Model Security Database

The Vision-Language Model (VLM) perception module in Vision-and-Language Navigation (VLN) agents is vulnerable to adversarial 3D object injection via the Adversarial Object Fusion (AdvOF) framework. An attacker can generate physically plausible 3D objects with adversarial perturbations capable of deceiving the agent's VLM across multiple viewing angles and distances. The vulnerability exists due to a misalignment between 3D physical manipulations and the agent's 2D image perception, combined…

A vulnerability in Large Language Models (LLMs) allows adversarial prompt distillation from a large language model (LLM) to a smaller language model (SLM), enabling efficient and stealthy jailbreak attacks. The attack leverages knowledge distillation techniques, reinforcement learning, and dynamic temperature control to transfer the LLM's ability to bypass safety mechanisms to a smaller, more easily deployable SLM. This allows for lower computational cost attacks with a potentially high…

Vision-Language Models (VLMs) contain a vulnerability in their multimodal fusion layers where safety-relevant information is linearly separable in the latent space. This allows for a "JailBound" attack, which exploits the implicit internal safety decision boundary. The attack proceeds in two stages: (1) Safety Boundary Probing, where attackers approximate the internal decision hyperplane by training layer-wise logistic regression classifiers on the fusion representations of safe versus unsafe…

Large Language Models (LLMs), specifically Llama-2-7B-Chat and Qwen2.5-Instruct (1.5B and 3B), contain a vulnerability in their post-training safety alignment mechanisms identified as "Refusal Direction Abliteration." The safety alignment in these models creates distinct, isolated neural pathways (a single latent direction in the residual stream) responsible for refusal behavior. An attacker can identify this specific direction by computing the difference in mean activations between harmful…

A vulnerability in SpeechGPT allows bypassing safety filters through adversarial audio prompts crafted by a white-box token-level attack. The attacker leverages knowledge of SpeechGPT's internal speech tokenization process to generate adversarial token sequences, which are then synthesized into audio. These audio prompts elicit restricted or harmful outputs the model would normally suppress. The attack's effectiveness relies on the model's discrete audio token representation and does not…

Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) prompting are vulnerable to input perturbations that decouple intermediate reasoning from the final answer. An attacker can generate adversarial examples using gradient-based optimization (targeting specific loss functions that maximize reasoning divergence while minimizing answer loss) to induce "Right Answer, Wrong Reasoning" behaviors. This vulnerability manifests through two primary attack vectors: 1. Token-level perturbations…

Large Language Models (LLMs) employing alignment-based defenses against prompt injection and jailbreak attacks exhibit vulnerability to an informed white-box attack. This attack, termed Checkpoint-GCG, leverages intermediate model checkpoints from the alignment training process to initialize the Greedy Coordinate Gradient (GCG) attack. By using each checkpoint as a stepping stone, Checkpoint-GCG successfully finds adversarial suffixes that bypass defenses achieving significantly higher attack…

Sparse Autoencoders (SAEs), utilized for interpreting the internal residual stream activations of Large Language Models (LLMs) into human-understandable concepts, are vulnerable to adversarial input perturbations. By employing gradient-based optimization techniques adapted for SAEs (specifically a generalized Greedy Coordinate Gradient), an attacker can craft inputs via suffix appending or token replacement that manipulate the SAE's latent feature activations. This vulnerability allows for the…

Speech-LLMs Qwen2-Audio (7B-Instruct) and Granite-Speech (3.2-8b) are vulnerable to universal acoustic adversarial attacks. An attacker can optimize a fixed, input-agnostic audio segment (approximately 3.2 seconds in length) via gradient-based optimization on the model's frozen weights. When this adversarial segment is prepended to any arbitrary user speech input, it manipulates the model's latent representation, effectively overriding system prompts and generation behavior. This vulnerability…

An adversarial audio perturbation vulnerability exists in open-source Speech Language Models (SLMs), specifically Qwen2-Audio-7B-Instruct and LLaMa-Omni. The vulnerability allows remote attackers to bypass safety alignment mechanisms and jailbreak the model by injecting imperceptible adversarial noise into audio prompts. By utilizing white-box access and Projected Gradient Descent (PGD) optimization, an attacker can manipulate the continuous speech signal to trigger harmful responses (e.g…