Language Model Security Database

Large Language Models (LLMs), such as GPT-4, exhibit a cross-lingual vulnerability in their safety mechanisms. Translating unsafe English prompts into low-resource languages, using readily available translation APIs like Google Translate, bypasses the LLM's safety filters and elicits harmful responses with a significantly higher success rate than attacks targeting the English language directly. The vulnerability stems from an unequal distribution of safety training data across languages…

A prompt-based adversarial attack, termed PromptAttack, can cause Large Language Models (LLMs) to generate incorrect outputs by manipulating the input prompt. PromptAttack crafts prompts that include the original input, an attack objective (to generate semantically similar but misclassified output), and attack guidance with instructions for character, word, or sentence-level perturbations. This allows an attacker to manipulate an LLM's response without direct access to its internal parameters…

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.

A universal black-box jailbreaking vulnerability exists in Large Language Models (LLMs) due to their susceptibility to adversarial prompts crafted using a genetic algorithm (GA). The GA optimizes a universal adversarial prompt suffix that, when appended to various user inputs, causes the LLM to generate unintended and potentially harmful outputs, bypassing safety mechanisms. This attack requires no knowledge of the LLM's internal architecture or parameters.

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 Models (LLMs) such as GPT-4, while employing safety alignment techniques, exhibit vulnerability to "CipherChat" attacks. CipherChat leverages cipher prompts (e.g., ASCII, Unicode, Caesar cipher, Morse code) combined with system role descriptions and few-shot enciphered demonstrations to bypass safety mechanisms trained on natural language. This allows an attacker to elicit unsafe responses from the LLM, effectively evading safety filters. The vulnerability is amplified by the…

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…

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.

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…