Language Model Security Database

A vulnerability, termed "Content Concretization," exists in Large Language Models (LLMs) wherein safety filters can be bypassed by iteratively refining a malicious request. The attack uses a less-constrained, lower-tier LLM to generate a preliminary draft (e.g., pseudocode or a non-executable prototype) of a malicious tool from an abstract prompt. This "concretized" draft is then passed to a more capable, higher-tier LLM. The higher-tier LLM, when prompted to refine or complete the existing…

Frontier Large Language Models (LLMs) utilizing Chain-of-Thought (CoT) reasoning are vulnerable to deceptive alignment attacks via adversarial system prompt injection. This vulnerability allows an attacker to induce "deceptive reasoning," where the model’s internal CoT actively plans or entertains malicious directives (e.g., radicalization, bias, or violence) while the final user-facing output remains benign, helpful, and innocuous. By creating a dissociation between internal reasoning and…

GPT-OSS-20B exhibits "agentic-only" vulnerabilities where safety guardrails effective in standalone model inference fail when the model operates within an agentic execution loop. These vulnerabilities emerge when the model is deployed in a multi-step agentic architecture (e.g., utilizing LangGraph, tool usage, and memory retention). Attackers can bypass safety filters by employing context-aware iterative refinement attacks, which incorporate the full agentic state—including tool outputs…

A vulnerability exists in multiple Large Language Models (LLMs) where an attacker can bypass safety alignments by exploiting the model's ethical reasoning capabilities. The attack, named TRIAL (Trolley-problem Reasoning for Interactive Attack Logic), frames a harmful request within a multi-turn ethical dilemma modeled on the trolley problem. The harmful action is presented as the "lesser of two evils" necessary to prevent a catastrophic outcome, compelling the model to engage in utilitarian…

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…

A vulnerability exists in multiple Large Language Models (LLMs) where safety alignment mechanisms can be bypassed by reframing harmful instructions as "learning-style" or academic questions. This technique, named Hiding Intention by Learning from LLMs (HILL), transforms direct, harmful requests into exploratory questions using simple hypotheticality indicators (e.g., "for academic curiosity", "in the movie") and detail-oriented inquiries (e.g., "provide a step-by-step breakdown"). The attack…

Aligned Large Language Models (LLMs) utilizing Transformer architectures are vulnerable to representation-level attacks targeting safety-knowledge neurons within the Multi-Layer Perceptron (MLP) layers. Research indicates that safety decision-making (Rejection vs. Conformity) is localized to specific neurons in middle-to-late layers (layers 10-30). An attacker with white-box access can calculate a "Conformity" direction vector based on the activation differences between benign and harmful…

Large Language Models (LLMs), including proprietary and open-weight state-of-the-art systems, are vulnerable to automated, self-evolving adversarial attacks orchestrated by multi-agent frameworks. The vulnerability exists because current safety alignment strategies (RLHF, static safety filters) fail to generalize against the "SafeEvalAgent" attack vector. In this vector, an "Analyst" agent analyzes model refusals to iteratively refine attack strategies, while a "Specialist" agent grounds these…

Large Language Model (LLM) inference APIs that expose top-k logits or log-probabilities are vulnerable to model extraction and cloning. An attacker can execute a two-stage attack to replicate the proprietary model without access to weights, gradients, or training data. First, by submitting fewer than 10,000 random queries and aggregating the returned unrounded logits, the attacker recovers the model's output projection matrix using Singular Value Decomposition (SVD). Second, the attacker…

Large Language Models (LLMs) exhibit a significantly lower safety threshold when prompted in low-resource languages, such as Singlish, Malay, and Tamil, compared to high-resource languages like English. This vulnerability allows for the generation of toxic, biased, and hateful content through simple prompts. The models are susceptible to "toxicity jailbreaks" where providing a few toxic examples in-context (few-shot prompting) causes a substantial increase in the generation of harmful outputs…