Language Model Security Database

Safety alignment degradation occurs in instruction-tuned Large Language Models (LLMs), specifically Llama-2-7B, Llama-3.2-1B, Qwen2.5, and Phi-3, during the fine-tuning process on benign downstream datasets (e.g., Dolly, Alpaca). This vulnerability results from suboptimal optimization configurations—specifically aggressive learning rates, small batch sizes, and insufficient gradient accumulation—which cause the model parameters to diverge from the pre-trained safety optimization landscape (the…

Large Language Models (LLMs) implementing "Thinking Mode" (also known as Reasoning Mode or Chain-of-Thought) exhibit a heightened susceptibility to jailbreak attacks compared to their non-reasoning counterparts. When a model is prompted to reason step-by-step (often delimited by specific tokens like <think> and </think>), the internal reasoning process frequently overrides safety alignment training. Research indicates that during the generation of the thinking chain, the model often…

Large Language Models (LLMs), including Llama 2, Mistral, and Vicuna, are susceptible to a white-box adversarial attack that circumvents safety alignment mechanisms (such as RLHF). The vulnerability exists due to the models' susceptibility to intrinsic optimization of adversarial suffixes using Exponentiated Gradient Descent (EGD). Unlike previous methods that rely on inefficient discrete token searches (e.g., Greedy Coordinate Gradient) or standard projected gradient descent, this attack…

Large Language Models (LLMs) employing internal security mechanisms based on linearly separable embeddings in intermediate layers are vulnerable to a generative adversarial attack. The CAVGAN framework exploits this vulnerability by generating adversarial perturbations that misclassify malicious inputs as benign, allowing the attacker to bypass the LLM's safety filters and elicit harmful outputs.

Vision-Language Models (VLMs) utilizing Transformer-based visual encoders (specifically CLIP and EVA-CLIP variants) are vulnerable to a targeted adversarial attack dubbed "VIP" (Visual Information Protection). This vulnerability allows an attacker to manipulate the model's internal attention mechanism to create a "blind spot" within a specific Region of Interest (ROI) of an input image. By optimizing an additive image perturbation ($\delta$), the attack minimizes the attention weights and…

Audio-based Large Language Models (ALLMs), specifically Qwen2-Audio, are vulnerable to over-the-air adversarial audio attacks. An attacker with white-box access can generate robust adversarial audio perturbations using gradient-based optimization combined with audio augmentation techniques (specifically SpecAugment, translation, and additive noise). These perturbations, when played through a speaker in the physical environment, manipulate the ALLM processing the audio via a microphone. This…

Reasoning-capable Large Language Models (LLMs) are vulnerable to a class of indirect prompt injection known as Copy-Guided Attacks (CGA). This vulnerability exploits the intrinsic behavior of reasoning models to copy tokens from the input prompt (such as variable names, function identifiers, or code snippets) into their intermediate reasoning traces (Chain-of-Thought). By embedding adversarial trigger sequences into external payloads—specifically within data the model is expected to analyze—an…

A resource consumption vulnerability exists in multiple Large Vision-Language Models (LVLMs). An attacker can craft a subtle, imperceptible adversarial perturbation and apply it to an input image. When this image is processed by an LVLM, even with a benign text prompt, it forces the model into an unbounded generation loop. The attack, named RECALLED, uses a gradient-based optimization process to create a visual perturbation that steers the model's text generation towards a predefined…

Adversarial Activation Patching enables the induction of emergent deceptive behaviors in safety-aligned transformer-based Large Language Models (LLMs). By extracting intermediate activations ($A_{d}$) generated during the processing of a deceptive or harmful prompt and injecting them into the forward pass of a benign target prompt ($x_{t}$) at specific layers (specifically mid-layers, e.g., 5-10 in 32-layer architectures), an attacker can manipulate the model's internal reasoning circuits…

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…