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…

Multiple open-weight Large Language Models (LLMs)—specifically those prioritizing capability over safety alignment—exhibit a critical vulnerability to adaptive multi-turn prompt injection and jailbreak attacks. While these models effectively reject isolated, single-turn adversarial inputs (averaging ~13.11% Attack Success Rate), they fail to maintain safety guardrails and policy enforcement across extended conversational contexts. By leveraging iterative strategies such as "Crescendo" (gradual…

Large Language Models (LLMs), including GPT-4o, LLaMA-3, and GPT-3.5-Turbo, are vulnerable to multimodal prompt injection attacks. These models fail to distinguish between system-level instructions and user-provided content within the context window. Attackers can exploit this by embedding malicious instructions in direct text, indirect sources (such as third-party webpages or PDFs), or visual inputs (images). Successful exploitation results in the model prioritizing the injected adversarial…

Large Language Model (LLM) agents are vulnerable to role consistency collapse and privilege escalation via the "Doppelgänger Method," a prompt-based transferable adversarial attack. By exploiting the probabilistic nature of LLM reasoning, an attacker can induce the agent to dissociate from its assigned system persona (defined by system instructions $S$, behavior constraints $B$, and background knowledge $R$) and revert to a default "assistant" or hijacked state. This vulnerability allows…

Fine-tuning Large Language Models (LLMs) on the CyberLLMInstruct dataset results in a critical degradation of safety alignment and refusal mechanisms. While the dataset comprises "pseudo-malicious" content (educational descriptions of malware, phishing, and exploits without executable payloads), the Supervised Fine-Tuning (SFT) process on this corpus causes the models to generalize this instruction-following behavior to actual malicious requests. This effectively bypasses safety guardrails…

A vulnerability in LLM-based agents, dubbed AI Agent Injection (AI²), allows attackers to hijack the agent's actions by manipulating the agent's memory retrieval mechanism. The attack involves two main steps: (1) Stealing action-aware knowledge from the agent's memory using crafted adversarial queries targeting the retriever module and (2) Generating Trojan prompts consisting of a Trojan string and hijacking instructions. The Trojan string is designed to manipulate the retriever into…

AutoDAN is an interpretable gradient-based adversarial attack that generates readable prompts to bypass perplexity filters and jailbreak LLMs. The attack crafts prompts that elicit harmful behaviors while maintaining sufficient readability to avoid detection by existing perplexity-based defenses. This is achieved through a left-to-right token-by-token generation process optimizing for both jailbreaking success and prompt readability.