Language Model Security Database

A vulnerability exists in OpenAI's Custom GPTs platform where the lack of effective isolation between the system context ("Expert Prompt"), external knowledge retrieval, and user input allows for unauthorized information disclosure and tool misuse. By employing specific prompt injection techniques—including Hex injection, Many-shot prefix attacks, and Knowledge Poisoning (uploading malicious files)—an attacker can bypass safety guardrails. This results in the extraction of proprietary system…

A safety bypass vulnerability exists in Large Language Models (LLMs) and Multi-Modal Models (LVLMs) that expose a "Prefix Completion" parameter in their inference APIs (e.g., echo or prefix parameters). The vulnerability exploits the model's autoregressive nature, where the model prioritizes local coherence with a user-supplied output prefix over global safety alignment (RLHF) or system instructions. By supplying a prefix that establishes a hostile narrative or intent (e.g., "My actions are as…

A vulnerability exists in multiple Large Language Models (LLMs) that allows for safety alignment bypass through an advanced jailbreaking technique called Template and Suffix Optimization (TASO). The attack combines two distinct optimization methods in an alternating, iterative feedback loop. First, a semantically meaningless adversarial suffix is optimized (e.g., using gradient-based methods like GCG) to force the LLM to begin its response with an affirmative phrase (e.g., "Sure, here is...")…

Improper restriction of the "Capability Space" in Large Language Model (LLM) applications allows remote attackers to manipulate application behavior through "Goal Deviation" attacks. This vulnerability arises when developers rely on the broad capabilities of a foundational model (e.g., GPT-4, LLaMA) without implementing sufficient negative constraints or disabling default plugins (e.g., DALL-E, Web Search) in the system prompt. Attackers can exploit this via natural language inputs to trigger…

OpenVLA, a Vision-Language-Action (VLA) model, contains a vulnerability regarding multimodal adversarial robustness. The model lacks sufficient cross-modal alignment stability, allowing attackers to disrupt the grounding between visual perception and linguistic instructions. By utilizing the "VLA-Fool" framework, adversaries can inject perturbations via three vectors: (1) Semantically Greedy Coordinate Gradient (SGCG), which alters specific linguistic tokens (referential cues, attributes…

Large Language Models (LLMs) are vulnerable to a black-box jailbreak technique known as the "Game-Theory Attack" (GTA). This vulnerability exploits the model's instruction-following and objective-shaping capabilities by framing the interaction as a finite-horizon sequential stochastic game. By introducing a game-theoretic scenario template—specifically a "Mechanism-Induced Graded Prisoner’s Dilemma"—the attacker alters the model's effective payoff structure. The vulnerability triggers a…

Embodied Artificial Intelligence (AI) agents utilizing Vision-Language Models (VLMs) for perception and planning are vulnerable to Indirect Environmental Jailbreak (IEJ). The vulnerability arises from the system's failure to distinguish between user-issued instructions and text embedded in the physical environment (e.g., writing on walls, sticky notes, or projections). The VLM processes visual text detected in the camera feed as authoritative context or direct commands, allowing a black-box…

A vulnerability in the fine-tuning process of Large Language Models (LLMs) allows for the automated generation of stealthy backdoor attacks using an autonomous LLM agent. This method, termed AutoBackdoor, creates a pipeline to generate semantically coherent trigger phrases and corresponding poisoned instruction-response pairs. Unlike traditional backdoor attacks that rely on fixed, often anomalous triggers, this technique produces natural language triggers that are contextually relevant and…

Large Language Models (LLMs) are vulnerable to a novel class of jailbreak attacks generated through the evolutionary synthesis of executable, code-based attack algorithms. Unlike traditional methods that refine or combine static prompts, this technique uses an automated multi-agent system (EvoSynth) to autonomously engineer and evolve the underlying code that generates the attack. These generated algorithms exhibit high structural and dynamic complexity, using features like control flow, state…

Centralized Multi-Agent Systems (MAS) architectures, specifically those implemented using frameworks such as Magentic-One, LangGraph, and OpenAI Agents, contain design-level vulnerabilities facilitating safety alignment bypasses. Two primary mechanisms drive this vulnerability: 1. Context Fragmentation via Atomic Delegation: The orchestrator decomposes adversarial prompts into atomic, granular sub-tasks (e.g., "click button," "write function header") before delegating them to sub-agents. These…