Language Model Security Database

Large Language Models (LLMs) struggle to generate genuinely fallacious reasoning. When prompted to create a false procedure for a harmful task, the LLMs instead leak the correct, harmful procedure while incorrectly claiming it's false. This vulnerability allows bypassing safety mechanisms and eliciting harmful outputs.

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…

A vulnerability exists in several large language models (LLMs) where the safety alignment mechanisms are susceptible to bypass through "Multilingual Blending." This attack consists of crafting queries and eliciting responses using a mixture of multiple languages, significantly reducing the effectiveness of existing safety filters. The vulnerability stems from the models' ability to process and generate text in multiple languages, which, when combined in specific ways, can confuse the safety…

The Automated Progressive Red Teaming (APRT) framework exploits vulnerabilities in large language models (LLMs) by iteratively generating adversarial prompts. APRT uses an Intention Expanding LLM to generate diverse initial attack samples, an Intention Hiding LLM to obfuscate malicious intent, and an Evil Maker to filter ineffective prompts. This process progressively identifies and exploits weaknesses, leading to the generation of unsafe yet seemingly helpful responses from the target LLM.

The SoP framework allows for automated generation of jailbreak prompts, bypassing safety mechanisms in LLMs. SoP utilizes multiple automatically optimized "jailbreak characters" within a single prompt to persuade the LLM to generate harmful or undesirable content, even without any seed jailbreak templates. This vulnerability is demonstrated against GPT-3.5, GPT-4, and LLaMA-2.

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) employing safety measures like filters and alignment training remain vulnerable to information leakage via "Decomposition Attacks". These attacks decompose a malicious query into multiple benign sub-queries, eliciting responses from the LLM that, when aggregated, reveal sensitive information without triggering safety filters or producing directly harmful outputs.

Large Language Models (LLMs) exhibit vulnerability to jailbreak attacks when prompted using Arabic transliteration and Arabizi (Arabic chatspeak). While LLMs demonstrate robustness to standard Arabic prompts, even with prefix injection, the use of transliterated or Arabizi prompts bypasses safety mechanisms, leading to the generation of unsafe content. This is due to the model's learned associations with specific words in these non-standard forms, which differ from its understanding of the…

Large Vision Language Models (LVLMs) are vulnerable to a bi-modal adversarial prompt attack (BAP). BAP leverages a combined textual and visual prompt to bypass safety mechanisms and elicit harmful responses, even in models designed to resist single-modality attacks. The attack first introduces a query-agnostic adversarial perturbation to the visual prompt, making the model more likely to respond positively regardless of the text. Then, an LLM refines the textual prompt iteratively to achieve…