Language Model Security Database

LLM-based autonomous agents are vulnerable to malfunction amplification attacks. These attacks exploit the inherent instability of agents by inducing repetitive or irrelevant actions through various methods including prompt injection and adversarial perturbations, leading to agent malfunction and task failure. The attacks do not rely on overtly harmful actions, making them harder to detect with standard LLM safety mechanisms.

Embodied Large Language Models (LLMs) are vulnerable to manipulation via voice-based interactions, leading to the execution of harmful physical actions. Attacks exploit three vulnerabilities: (1) cascading LLM jailbreaks resulting in malicious robotic commands; (2) misalignment between linguistic outputs (verbal refusal) and physical actions (command execution); and (3) conceptual deception, where seemingly benign instructions lead to harmful outcomes due to incomplete world knowledge within…

Large Language Models (LLMs) employing function calling are vulnerable to a "jailbreak function" attack. Maliciously crafted function definitions and prompts can coerce the LLM into generating harmful content within the function's arguments, bypassing existing safety filters designed for chat modes. This exploits discrepancies in safety alignment between function argument generation and chat response generation.

Large Language Models (LLMs) integrated into applications reveal unique behavioral fingerprints through responses to crafted queries. LLMmap exploits this by sending carefully constructed prompts and analyzing the responses to identify the specific LLM version with high accuracy (over 95% in testing against 42 LLMs). This allows attackers to tailor attacks exploiting known vulnerabilities specific to the identified LLM version.

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) 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.

LLMs, even when individually assessed as "safe," can be combined by an adversary to achieve malicious outcomes. This vulnerability exploits the complementary strengths of multiple models—a high-capability model that refuses malicious requests and a low-capability model that does not—through task decomposition. Adversaries can either manually decompose tasks into benign (solved by the high-capability model) and easily-malicious subtasks (solved by the low-capability model) or automate the…