Language Model Security Database

Aligned Large Language Models (LLMs) exhibit a "compositional blindness" vulnerability wherein safety alignment mechanisms evaluate user prompts in isolation, failing to detect malicious intent when it is systematically decomposed into multiple benign-appearing sub-tasks. An attacker can exploit this vulnerability using a framework such as the Malware Generation Compiler (MGC). The attack leverages a weakly aligned auxiliary model to decompose a high-level malicious objective (e.g…

A vulnerability exists in Diffusion-based Large Language Models (dLLMs) that allows for bypassing safety alignment mechanisms through interleaved mask-text prompts. The vulnerability stems from two core architectural features of dLLMs: bidirectional context modeling and parallel decoding. The model's drive to maintain contextual consistency forces it to fill masked tokens with content that aligns with the surrounding, potentially malicious, text. The parallel decoding process prevents dynamic…

Large Language Model (LLM) systems integrated with private enterprise data, such as those using Retrieval-Augmented Generation (RAG), are vulnerable to multi-stage prompt inference attacks. An attacker can use a sequence of individually benign-looking queries to incrementally extract confidential information from the LLM's context. Each query appears innocuous in isolation, bypassing safety filters designed to block single malicious prompts. By chaining these queries, the attacker can…

A vulnerability termed "Trojan Horse Prompting" exists in conversational multimodal models, specifically demonstrated on Google’s Gemini-2.0-flash-preview-image-generation. The vulnerability allows an attacker to bypass safety alignment mechanisms (RLHF and SFT) by manipulating the structural protocol of the conversational API. Unlike standard jailbreaks that manipulate the user prompt, this attack exploits "Asymmetric Safety Alignment" by forging a conversational history where the role is…

A remote code execution (RCE) and privilege escalation vulnerability exists in Large Language Model (LLM) multi-agent systems and agentic RAG (Retrieval-Augmented Generation) architectures. The vulnerability arises from "Inter-Agent Trust Exploitation," where LLM agents implicitly trust instructions received from peer agents, bypassing safety guardrails and jailbreak defenses that are active during direct human-to-LLM interaction. An attacker can inject a malicious command payload (e.g., a…

Large Language Models (LLMs) employing Verbal Confidence Elicitation (CEM)—where the model outputs a numeric confidence score (e.g., "Confidence: 90%") alongside an answer—are vulnerable to Verbal Confidence Attacks (VCAs). Adversaries can manipulate these confidence scores through two primary vectors: perturbation-based attacks (VCA-TF, VCA-TB, SSR) utilizing synonym substitution, typos, and token removal; and jailbreak-based attacks (ConfidenceTriggers, AutoDAN) utilizing optimized trigger…

Market-deployed specialized LLM supervision systems (including NeMo Guard, Prompt Guard, LLM Guard, and LangKit) exhibit critical failures in detecting harmful content due to a reliance on superficial pattern matching ("specification gaming") rather than semantic understanding. These systems fail to generalize to inputs that do not match specific training patterns, resulting in near-zero detection rates for straightforward harmful prompts in categories such as CBRN (Chemical, Biological…

Large Language Models (LLMs) equipped with native code interpreters are vulnerable to Denial of Service (DoS) via resource exhaustion. An attacker can craft a single prompt that causes the interpreter to execute code that depletes CPU, memory, or disk resources. The vulnerability is particularly pronounced when a resource-intensive task is framed within a plausibly benign or socially-engineered context ("indirect prompts"), which significantly lowers the model's likelihood of refusal compared…

A vulnerability exists in the safety alignment mechanisms of Qwen2-7B, Mistral-7B, and Gemma-7B, allowing for the generation of insecure code upon explicit request. Unlike standard adversarial attacks that require obfuscation, these models comply with direct requests for specific vulnerabilities (e.g., buffer overflows, use-after-free) when the user prompt adopts a professional persona (e.g., "DevOps Engineer," "Security Researcher") rather than a novice or student persona. The models exhibit…

Large Language Models (LLMs) employing safety filters designed to prevent generation of content related to self-harm and suicide can be bypassed through multi-step adversarial prompting. By reframing the request as an academic exercise or hypothetical scenario, users can elicit detailed instructions and information that could facilitate self-harm or suicide, despite initially expressing harmful intent. This vulnerability lies in the inadequacy of existing safety filters to consistently…