Language Model Security Database

A vulnerability in the multi-turn context handling of Large Language Models (LLMs) allows attackers to bypass safety guardrails by decomposing complex fraud and cybercrime operations into a sequence of seemingly benign queries. By mapping the cybercrime lifecycle (planning, reconnaissance, falsification, engagement, evasion, and scaling) into Long-Form Tasks (LFTs) and framing the queries as legitimate research or security testing, attackers can elicit actionable attack materials and detailed…

Diffusion Large Language Models (D-LLMs) are vulnerable to a "Context Nesting" attack that bypasses safety alignment mechanisms. While D-LLMs typically utilize a stepwise reduction effect during the iterative denoising process to suppress harmful content, this mechanism fails when harmful requests are embedded within benign, structured contexts. By wrapping a malicious query inside high-level structural templates (such as code completion, table filling, JSON, or YAML formats), an attacker can…

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 multi-tenant LLM serving frameworks allows attackers to reconstruct the private prompts of other users via an active Key-Value (KV) cache side-channel. Frameworks that utilize shared KV caches alongside specific scheduling policies, such as Longest Prefix Match (LPM), prioritize waiting requests based on the length of their matched prefix tokens. An attacker can exploit this by iteratively sending batches of guessed tokens mixed with dummy queries. If a guessed token matches…

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…

A vulnerability exists in Large Language Model (LLM) safety alignment mechanisms where the combination of Task-Oriented Prompts (ToP) and few-shot demonstrations significantly degrades defense effectiveness against jailbreak attacks. When few-shot examples (in-context learning) are appended to system prompts that explicitly define safety as a task objective (ToP), the model's attention to the safety instruction is diluted due to the "lost in the middle" phenomenon and attention entropy growth…

LLM serving frameworks utilizing continuous batching and PagedAttention (such as vLLM, SGLang, and Orca) are vulnerable to a resource exhaustion Denial-of-Service attack known as "Fill and Squeeze." An unprivileged remote attacker can exploit the deterministic state transitions of the scheduler's memory management to induce severe latency or service denial. The attack leverages a side-channel vulnerability where Inter-Token Latency (ITL) correlates linearly with global KV-cache usage due to…

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…

Large Language Models (LLMs), specifically LLaMA-3.2-11B, Qwen3-14B, Gemma 3-12B, and GPT-oss-20B, are vulnerable to black-box jailbreaking attacks via history-guided reinforcement learning (RL). The vulnerability arises from the models' inability to detect adversarial intent when prompts are iteratively refined based on historical interaction signals. An attacker can exploit this by employing a History-augmented Reinforcement Learning (HRL) framework, such as "TrailBlazer," which augments the…