Language Model Security Database

A vulnerability exists in multimodal Large Language Models (LLMs) integrated with external tools. Adversarial images, visually indistinguishable from benign images, can manipulate the LLM to execute unintended tool commands, compromising the confidentiality and integrity of user resources. The attack is effective across diverse prompts, remaining stealthy both in the image itself and in the generated text response.

Large Language Models (LLMs) employing alignment techniques remain vulnerable to "jailbreak" attacks. The AutoDAN technique automatically generates semantically meaningful prompts that bypass safety features and elicit malicious outputs from aligned LLMs, unlike previous methods producing nonsensical prompts easily detectable by perplexity checks. These prompts exploit weaknesses in the LLM's alignment, causing it to generate responses that violate intended safety constraints.

Large Language Models (LLMs) are susceptible to automated jailbreak attacks using a fuzzing framework that generates variations of existing jailbreak prompts. This vulnerability allows bypassing built-in safety mechanisms, leading to the generation of harmful or unintended outputs. The vulnerability stems from the LLMs' inability to consistently recognize and reject semantically similar, but subtly different prompt variations generated through automated mutation techniques.

Large Language Models (LLMs) are vulnerable to a "Chain of Utterances" (CoU) based prompt injection attack. This attack exploits the LLM's ability to engage in multi-turn conversations and role-playing, tricking it into providing harmful or unsafe responses even when presented with safety guidelines. The attack leverages a crafted conversation between two agents ("Red-LM," a malicious agent, and "Base-LM," a seemingly helpful agent) to elicit unethical responses from the Base-LM by subtly…

Large Language Model (LLM)-integrated web applications using Langchain (and potentially similar middleware) are vulnerable to Prompt-to-SQL (P2SQL) injection attacks. Unsanitized user prompts can be crafted to cause the LLM to generate malicious SQL queries, leading to unauthorized database access (read and write operations). This vulnerability bypasses attempts to restrict the LLM through prompt engineering alone.

Aligned large language models (LLMs) are vulnerable to a universal and transferable adversarial suffix attack. Appending a specific, automatically generated suffix to a wide range of prompts, even those requesting objectionable content, causes the models to generate harmful or objectionable responses instead of refusing the request. The attack's success rate is significantly higher on GPT-based models.

The MASTER KEY framework exploits timing-based characteristics of Large Language Model (LLM) chatbot responses to infer internal defense mechanisms and automatically generate jailbreak prompts. This allows bypassing safety restrictions and eliciting responses violating usage policies, including generation of illegal, harmful, privacy-violating, and adult content. The framework utilizes a three-step process: reverse-engineering defenses via time-based analysis, creating proof-of-concept…

A prompt injection vulnerability allows attackers to manipulate the behavior of Large Language Model (LLM)-integrated applications by crafting malicious prompts that override the application's intended functionality. Attackers can achieve this by constructing prompts that cause the LLM to interpret malicious payloads as instructions, rather than data, leading to unintended actions such as data leakage, unauthorized LLM usage, or application mimicry. This vulnerability exploits the way user…

Large Language Models (LLMs), specifically ChatGPT versions 3.5 and 4.0, are vulnerable to prompt engineering attacks that circumvent built-in content restrictions. Attackers can craft malicious prompts, categorized into "pretending," "attention shifting," and "privilege escalation" techniques, to elicit responses containing prohibited content (e.g., instructions for illegal activities, generation of harmful content). The vulnerability stems from the LLM's inability to reliably distinguish…