Language Model Security Database

Autoregressive Large Language Models (LLMs) utilizing In-Context Learning (ICL) are vulnerable to demonstration permutation attacks due to inherent sensitivity to the ordering of input examples. This vulnerability arises from the limitations of unidirectional attention mechanisms and standard Empirical Risk Minimization (ERM) training, which fails to account for worst-case input permutations. An attacker can exploit this by permuting the order of valid, semantically correct few-shot…

A policy compliance vulnerability exists in the OpenAI GPT Store ecosystem affecting Custom GPTs. The vulnerability stems from the inheritance of safety alignment weaknesses from foundational models (GPT-4 and GPT-4o) and the insufficient enforcement of usage policies during the customization and review process. Custom GPTs can be trivially manipulated to violate safety guidelines—specifically regarding Cybersecurity (malware generation), Academic Integrity (ghostwriting), and Romantic…

A vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs) related to the model's intent perception capabilities. The specific attack vector, termed "Perceived-importance Flatten" (PiF), circumvents safety guardrails by modifying neutral-intent tokens within a malicious prompt using synonym replacement. Unlike traditional jailbreak attacks that rely on appending lengthy, high-perplexity adversarial suffixes (which suffer from distributional dependency and often…

Large Reasoning Models (LRMs) are vulnerable to a novel jailbreak attack, "Mousetrap," which leverages the models' reasoning capabilities to elicit harmful responses. Mousetrap uses a "Chaos Machine" to iteratively transform prompts via one-to-one mappings (e.g., character substitutions, word reversals), creating complex reasoning chains that confuse the LRM and cause it to generate unsafe outputs despite safety mechanisms. The iterative nature of the attack, combined with role-playing…

A vulnerability exists in Large Language Models (LLMs) that allows for efficient jailbreaking by selectively fine-tuning only the lower layers of the model with a toxic dataset. This "Freeze Training" method, as described in the research paper, concentrates the fine-tuning on layers identified as being highly sensitive to the generation of harmful content. This approach significantly reduces training duration and GPU memory consumption while maintaining a high jailbreak success rate.

A resource exhaustion and algorithmic complexity vulnerability exists in applications utilizing Reasoning Large Language Models (e.g., OpenAI o1, DeepSeek R1) that process untrusted external context (such as Retrieval-Augmented Generation systems). The vulnerability, dubbed "OverThink," allows an attacker to perform an indirect prompt injection by embedding "decoy" reasoning problems—specifically computation-intensive tasks like Sudoku puzzles or Markov Decision Processes (MDPs)—into the…

Large Language Models (LLMs) with refusal training are vulnerable to a "jailbreaking-to-jailbreak" (J2) attack. A J2 attack involves initially jailbreaking a powerful LLM to create a "J2 attacker." This attacker, instructed with general jailbreaking strategies, then autonomously attempts to jailbreak other LLMs, including potentially the same model it was derived from, by iteratively refining its attack based on previous attempts and in-context learning.

Large Language Models (LLMs) are vulnerable to structure transformation attacks, where malicious prompts are encoded in diverse syntax spaces (e.g., SQL, JSON, LLM-generated syntaxes) to bypass safety mechanisms. These attacks maintain the harmful intent while altering the linguistic structure, making detection based on token-level patterns ineffective.

Large Language Model (LLM) watermarking schemes based on n-gram probability biases (specifically KGW, SynthID-Text, MinHash, and SkipHash) are vulnerable to adversarial removal during Knowledge Distillation. When a student model is trained on the output of a watermarked teacher model, it inherits the watermark's statistical biases ("radioactivity"). An attacker can exploit this inheritance by comparing the student model's output token probabilities against a base model to extract the…

Large Language Models (LLMs) are vulnerable to "Rewrite to Jailbreak" (R2J) attacks. R2J exploits the models' safety mechanisms by iteratively rewriting harmful prompts, subtly altering wording to bypass safety filters while maintaining the original malicious intent. This differs from previous methods which rely on adding extraneous prefixes/suffixes or creating forced instruction-following scenarios, thus being more difficult to detect.