Language Model Security Database

Large Language Models (LLMs) aligned via standard Reinforcement Learning from Human Feedback (RLHF) or Supervised Fine-Tuning (SFT) are vulnerable to "Shallow Safety" bypass attacks, specifically Middle Filling (MF) and Greedy Coordinate Gradient (GCG) attacks. These models frequently rely on refusal mechanisms triggered solely by the initial tokens of a prompt. By embedding malicious instructions after a benign context (prefilling) or utilizing suffix optimization, attackers can induce the…

Reinforcement learning (RL) based post-training for explicit chain-of-thought reasoning (e.g., GRPO) in Multimodal Large Reasoning Models (MLRMs) inadvertently degrades safety alignment, rendering the models highly vulnerable to multimodal jailbreak attacks. The vulnerability is caused by "conditional coverage collapse" during the initial phases of chain-of-thought generation. Under adversarial conditioning (text or image), the reasoning policy assigns vanishing probability mass to safe…

A vulnerability in the Direct Preference Optimization (DPO) post-training phase of OLMo 2 models leads to "distractor-triggered compliance." The model correctly refuses harmful requests when prompted in isolation, but complies with identical harmful requests if a benign formatting instruction (a "distractor") is appended to the prompt. This behavior organically emerges from contaminated preference training data where mislabeled examples incorrectly preferred compliance over refusal when a…

Large Vision-Language Models (LVLMs) are vulnerable to zero-query, black-box adversarial image perturbations via Semantic-Guided Multimodal Attacks (SGMA). Unlike traditional attacks that scatter noise or target background pixels, SGMA leverages surrogate models (e.g., CLIP) to anchor imperceptible adversarial perturbations directly onto semantically critical foreground regions. The attack exploits two specific architectural traits of LVLMs: inconsistent visual grounding across models and…

Large Language Models (LLMs) subjected to machine unlearning techniques (specifically AltPO, GradDiff, IDKDPO, IDKNLL, UNDIAL, NPO, and SimNPO) contain a vulnerability regarding the persistence of latent knowledge. Despite achieving high "forgetting" scores on standard, benign benchmarks, these models remain susceptible to black-box evolutionary adversarial attacks. An attacker can utilize an automated framework (REBEL) comprising a "Hacker" model and a "Judge" model to iteratively mutate…

Large Language Models (LLMs) and Large Reasoning Models (LRMs) are vulnerable to an inference-time jailbreak attack known as "Hybrid Gradient Shift." This vulnerability exploits the statistical discrepancy between an aligned model's output distribution and a pre-alignment reference distribution. By treating safety alignment as a form of miscalibration, an attacker can aggregate the logits of a target model (aligned), a helper model (unaligned/weak), and a predictor model to reconstruct the…

Multiple open-source Large Language Models (LLMs) in the 1B to 7B parameter range are vulnerable to safety bypasses via long-form, multi-step reasoning prompt injection and jailbreak attacks. Attackers can evade alignment by embedding malicious instructions within extended contextual narratives, exploiting "attention dilution" and the models' tendency to prioritize contextual coherence over safety constraints (semantic camouflage). These reasoning-heavy attacks consistently bypass standard…

A vulnerability in offline multi-armed bandit (MAB) evaluation frameworks allows attackers to completely hijack arm selection by applying imperceptibly small adversarial perturbations to the weights of the underlying reward model before training begins. Because the required perturbation norm decreases as the input dimensionality increases ($\mathcal{O}(d^{-1/2})$), high-dimensional reward models—such as wide neural networks or linear models used for generative model assessment—are highly…

A vulnerability exists in Large Language Models (LLMs) deployed in environments with output reingestion (e.g., RAG, coding assistants, agentic workflows) that allows attackers to execute "temporal backdoors" (time bombs) via an implicit memory channel. Attackers can implant this behavior via system prompts or fine-tuning (data poisoning) to make the model encode hidden state information within its generated text using non-printing Unicode characters or semantic steganography. When these…

Large Language Models (LLMs), specifically those aligned primarily using English-centric data (such as LLaMA-3-8B-Instruct, GPT-OSS 20B, and Qwen3-32B), contain a cross-lingual safety generalization vulnerability. Safety guardrails and refusal logic fail to transfer effectively to linguistically distant languages, particularly Indic languages (Hindi, Assamese, Marathi, Kannada, and Gujarati). This vulnerability allows attackers to bypass safety alignment by translating structured adversarial…