Language Model Security Database

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…

Mobile LLM agents utilizing vision-based screen perception (OCR or Multimodal Large Language Models) are vulnerable to Visual Prompt Injection via malicious GUI overlays. An attacker holding the SYSTEM_ALERT_WINDOW permission can deploy non-focusable floating windows (using FLAG_NOT_FOCUSABLE) containing adversarial text or fabricated UI elements over legitimate applications. Because the agent captures the entire screen buffer to interpret the device state, it ingests the adversarial overlay…

Large Vision-Language Models (LVLMs) that utilize a projection layer (adapter) to bridge a vision encoder and a Large Language Model (LLM) contain a vulnerability stemming from the "Modality Gap"—a distributional distance between image and text token embeddings. This gap allows the visual modality to bypass the safety alignment (RLHF/instruction tuning) of the backbone LLM. Attackers can trigger harmful, toxic, or illegal responses to queries that would be refused in text-only contexts by…

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…

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…

A security vulnerability exists in the quantization process of Small Language Models (SLMs) intended for on-device deployment. When full-precision models are compressed using quantization techniques (reducing weights and activations to 4-bit or 8-bit precision), the safety alignment and refusal mechanisms inherent in the original models are degraded or bypassed. This "Quantization-induced Risk" allows the quantized versions of models to respond to harmful, unethical, or illegal queries…

Large Audio-Language Models (LAMs) are vulnerable to adversarial signal-level perturbations that allow for the bypass of safety guardrails (jailbreaking). While these models may possess robust text-based safety alignment, they fail to generalize this robustness to the audio modality. Attackers can utilize the Audio Perturbation Toolkit (APT) to apply transformations in the time domain (Energy Distribution Perturbation, Trimming, Fade In/Out), frequency domain (Pitch Shifting, Temporal…

A vulnerability exists in Vision-Language Models (VLLMs) that allows for transferable, targeted adversarial attacks. Attackers can generate adversarial image perturbations using an ensemble of open-source surrogate models (primarily CLIP-based visual encoders) which effectively transfer to proprietary, black-box VLLMs. The attack leverages a specific optimization framework that combines a Visual Contrastive Loss with multiple positive/negative visual examples, rather than relying solely on…

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…

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…