Language Model Security Database

Large Language Models (LLMs) are vulnerable to adversarial attacks that utilize low-perplexity prompts to elicit unsafe content. These prompts, while statistically likely to occur in normal conversation, can trigger the generation of harmful or toxic outputs that evade standard safety filters. The vulnerability stems from the model's inability to reliably distinguish between benign and malicious intents within the statistical distribution of natural language.

Large Language Models (LLMs) used for code generation are vulnerable to Malicious Programming Prompts (MaPP), where an attacker injects a short string (under 500 bytes) into the prompt, causing the LLM to generate code containing vulnerabilities while maintaining functional correctness. The attack exploits the LLM's ability to follow instructions, even those inserted maliciously, to embed unintended behaviors. The injected code can range from general vulnerabilities (e.g., setting a…

Appending a single whitespace character (space) or certain punctuation marks to the end of an LLM's input template can bypass safety mechanisms and cause the model to generate unsafe, biased, or factually incorrect outputs, even if the original prompt was benign. This vulnerability is due to the statistical properties of single-character tokens in the model's training data, causing unintended behavior in the model's token prediction.

Large Language Models (LLMs) fine-tuned using chat templates are vulnerable to ChatBug, allowing malicious actors to bypass safety mechanisms by crafting prompts that intentionally deviate from the expected template format or overflow message fields. This exploits the LLM’s reliance on the template structure without enforcing similar constraints on user input.

Large Language Models (LLMs) exhibit increased vulnerability to adversarial prompts employing code-switching techniques, where multiple languages are interwoven within a single query. This vulnerability stems from an unintended correlation between the resource availability of the languages used in the prompt and the LLM's safety alignment. LLMs trained on imbalanced multilingual data are more susceptible to attacks leveraging low-resource languages, resulting in a higher rate of unsafe or…

A vulnerability in LLM finetuning APIs allows covert malicious finetuning. Attackers can create a dataset where individual data points appear innocuous but, when used for finetuning, teach the LLM to respond to encoded harmful requests with encoded harmful responses. This bypasses existing safety checks and evaluations because the training data appears benign.

Large Language Models (LLMs) used to control robots exhibit biases leading to discriminatory and unsafe behaviors. When provided with personal characteristics (e.g., race, gender, disability), LLMs generate biased outputs resulting in discriminatory actions (e.g., assigning lower rescue priority to certain groups) and accept or deem feasible dangerous or unlawful instructions (e.g., removing a person's mobility aid).

A vulnerability in Retrieval-Augmented Generation (RAG) systems utilizing LangChain allows for indirect jailbreaks of Large Language Models (LLMs). By poisoning the external knowledge base accessed by the LLM through LangChain, attackers can manipulate the LLM's responses, causing it to generate malicious or inappropriate content. The attack exploits the LLM's reliance on the external knowledge base and bypasses direct prompt-based jailbreak defenses.

Multimodal Large Language Models (LLMs) processing speech input are vulnerable to adversarial attacks. Imperceptible perturbations added to audio input can cause the model to generate unsafe or harmful text responses, overriding built-in safety mechanisms. The attacks are effective even with limited knowledge of the model's internal workings, demonstrating transferability across different models.

A vulnerability in large language models (LLMs) allows attackers to elicit unsafe or unethical responses through a chain of semantically relevant multi-turn prompts. The attack, termed "Chain of Attack" (CoA), exploits the model's contextual understanding and adaptive response capabilities to gradually steer the conversation towards the desired harmful output, even if single-turn prompts are rejected due to safety mechanisms. The attack leverages semantic similarity scoring (e.g., using…