Language Model Security Database

Large Language Models (LLMs) are vulnerable to jailbreaking attacks leveraging mutation-based fuzzing techniques. The TurboFuzzLLM framework efficiently generates adversarial prompts, combining mutated templates with harmful questions to elicit unauthorized or malicious responses. This vulnerability allows bypassing built-in safeguards and obtaining harmful outputs through black-box API access. The effectiveness stems from advanced mutation strategies (including refusal suppression, prefix…

JailbreakEdit is a novel attack that injects a universal jailbreak backdoor into safety-aligned Large Language Models (LLMs) by exploiting model editing techniques. The attack modifies specific parameters within the model's feed-forward networks, creating shortcuts that bypass internal safety mechanisms and trigger jailbroken responses to a wide range of prompts, including those containing sensitive or harmful content. The attack requires only one-time parameter modification, making it…

Standard Large Language Model (LLM) unlearning techniques, specifically Negative Preference Optimization (NPO), Gradient Difference (GradDiff), and Representation Misdirection for Unlearning (RMU), fail to sufficiently flatten the loss landscape surrounding the "forgotten" weights. This sharp loss landscape allows for a "Relearning Attack," wherein an attacker can fully restore the unlearned capabilities (such as hazardous knowledge, sensitive data, or copyrighted material) by performing…

Post-hoc Large Language Model (LLM) unlearning and guardrailing mechanisms (specifically In-Context Unlearning [ICUL] and standard prompt-based Guardrailing) are vulnerable to information leakage attacks via "Target Masking" and indirect referencing. These systems rely on superficial semantic matching to suppress "forget sets" (specific entities or concepts). Attackers can bypass these restrictions by querying associated properties, relationships, or pseudonyms rather than the explicit target…

State-of-the-art machine unlearning and safety fine-tuning methods for Large Language Models (LLMs) fail to robustly remove hazardous capabilities or refusal mechanisms from model weights. While these methods suppress model outputs during standard input-output interactions, the underlying capabilities remain latent in the parameter space. An attacker with access to model weights (e.g., via open releases or leaked weights) can restore "unlearned" knowledge (such as dual-use biology hazards) or…

The SMAB (Sensitivity-based Multi-Armed Bandit) framework introduces a vulnerability in text classifiers and Large Language Models (LLMs) by enabling efficient, black-box adversarial text generation. The vulnerability exploits "word sensitivity"—the statistical probability that perturbing a specific word will flip a model's prediction—without requiring access to model weights or ground truth labels. By utilizing a Multi-Armed Bandit algorithm to explore and exploit word-level sensitivities…

The Emoti-Attack vulnerability constitutes a zero-word-perturbation adversarial attack against Natural Language Processing (NLP) systems and Large Language Models (LLMs). The vulnerability exploits the discrete embedding space of emojis and emoticons to manipulate model behavior without altering the semantic content or character integrity of the original text. By appending strategically optimized emoji sequences to the prefix and suffix of an input string (formalized as $s \oplus x \oplus…

Vision Language Models (VLMs) integrated into autonomous driving (AD) systems are vulnerable to a black-box adversarial attack method termed Cascading Adversarial Disruption (CAD). The vulnerability stems from the model's susceptibility to optimized visual perturbations that disrupt the decision-making reasoning chain (perception, prediction, and planning). Attackers can generate adversarial images or physical patches by aligning visual noise with deceptive textual semantics in the model's…

A vulnerability exists in Large Language Model (LLM) routing systems (control planes) that allows for the manipulation of inference flow via adversarial input sequences. LLM routers, which dynamically direct user queries to either "weak" (cheaper) or "strong" (expensive) models based on predicted query complexity, can be bypassed by appending specific, pre-optimized token sequences known as "confounder gadgets." These gadgets artificially inflate the router's complexity score for an input…

A multi-step prompt injection vulnerability allows attackers to bypass Large Language Model (LLM) safety guardrails by combining prompt obfuscation with task decomposition. The attack methodology, identified as part of the CySecBench research, employs a "Word Reversal" technique where every fifth word in the malicious input is reversed to evade initial keyword detection. This obfuscated input is then embedded within a benign educational context, specifically instructing the model to act as a…