Language Model Security Database

Large Language Models (LLMs), specifically Qwen2.5-0.5B-Instruct, LLaMA-3.2-1B-Instruct, and GPT-OSS-20B, are vulnerable to gradient-based adversarial attacks, including the Greedy Coordinate Gradient (GCG) algorithm and its annealing-augmented variant, T-GCG. Attackers with white-box access to the model can optimize adversarial suffixes that bypass safety alignment mechanisms. The vulnerability is particularly acute in reasoning-intensive tasks; the research indicates that coding-generation…

IntentionReasoner, specifically the 1.5B and 3B parameter versions optimized via Reinforcement Learning (RL), contains a safety regression vulnerability where the RL alignment process degrades the model's resistance to jailbreak attacks compared to the Supervised Fine-Tuning (SFT) baseline. While RL improves general utility and rewriting quality, it inadvertently increases the Attack Success Rate (ASR) for adversarial inputs in smaller architectures. This allows sophisticated jailbreak prompts…

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…

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…

The Magic-Token-Guided Co-Training (MTC) framework for Large Language Models (LLMs) introduces a mechanism where distinct behavioral modes are activated via hardcoded system-level strings known as "magic tokens." A specific vulnerability exists in the implementation of the "negative" (neg) behavior mode, which is explicitly trained to generate unfiltered, risk-prone, and harmful content for internal red-teaming. The framework relies on the secrecy of the magic token (e.g., a random string like…

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…

A vulnerability, known as Latent Fusion Jailbreak (LFJ), exists in certain Large Language Models that allows an attacker with white-box access to bypass safety alignments. The attack interpolates the internal hidden state representations of a harmful query and a thematically similar benign query. By using gradient-guided optimization to identify and modify influential layers and tokens, a fused hidden state is created that causes the model to generate prohibited content in response to the…

Audio-Language Models (ALMs) including Qwen2.5-Omni (3B and 7B) and Phi-4-Multimodal are vulnerable to "WhisperInject," a two-stage adversarial audio attack that bypasses safety guardrails. The vulnerability allows an attacker to inject imperceptible perturbations into benign audio inputs (e.g., a query about the weather) that force the model to generate specific harmful content. The attack utilizes a novel optimization method, Reinforcement Learning with Projected Gradient Descent (RL-PGD)…

A vulnerability exists in multiple Large Language Models (LLMs) that allows for safety alignment bypass through a technique named Activation-Guided Local Editing (AGILE). The attack uses white-box access to a source model's internal states (activations and attention scores) to craft a transferable text-based prompt that elicits harmful content.

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…