Language Model Security Database

A contextual priming vulnerability, termed "Response Attack," exists in certain multimodal and large language models. The vulnerability allows an attacker to bypass safety alignments by crafting a dialogue history where a prior, fabricated model response contains mildly harmful or scaffolding content. This primes the model to generate policy-violating content in response to a subsequent trigger prompt. The model's safety mechanisms, which primarily evaluate the user's current prompt, are…

Large Language Models (LLMs) utilizing Reinforcement Learning from Human Feedback (RLHF) and other safety alignment techniques are vulnerable to "CognitiveAttack," a jailbreak vector that exploits synergistic cognitive biases. The vulnerability exists because models internalize human-like reasoning fallacies during pre-training and alignment. Adversaries can bypass safety guardrails by rewriting harmful instructions to trigger specific psychological heuristics—specifically through the…

A vulnerability exists in Large Language Models, including GPT-3.5 and GPT-4, where safety guardrails can be bypassed using Trojanized prompt chains within a simulated educational context. An attacker can establish a benign, pedagogical persona (e.g., a curious student) over a multi-turn dialogue. This initial context is then exploited to escalate the conversation toward requests for harmful or restricted information, which the model provides because the session's context is perceived as safe…

Multimodal Large Language Models (MLLMs) are vulnerable to visual contextual attacks, where carefully crafted images and accompanying text prompts can bypass safety mechanisms and elicit harmful responses. The vulnerability stems from the MLLM's ability to integrate visual and textual context to generate outputs, allowing attackers to create realistic scenarios that subvert safety filters. Specifically, the attack leverages image-driven context injection to construct deceptive multi-turn…

Large Language Models (LLMs) are vulnerable to obfuscation-based jailbreak attacks using the MetaCipher framework. MetaCipher employs a reinforcement learning algorithm to iteratively select from a pool of 21 ciphers to encrypt malicious keywords within prompts, evading standard safety mechanisms that rely on keyword detection. The framework adaptively learns optimal cipher choices to maximize the success rate of the jailbreak, even against LLMs with reasoning capabilities. Successful attacks…

Large Language Models (LLMs) are vulnerable to jailbreaking through an agentic attack framework called Composition of Principles (CoP). This technique uses an attacker LLM (Red-Teaming Agent) to dynamically select and combine multiple human-defined, high-level transformations ("principles") into a single, sophisticated prompt. The composition of several simple principles, such as expanding context, rephrasing, and inserting specific phrases, creates complex adversarial prompts that can bypass…

Large Language Models (LLMs) are vulnerable to a novel adversarial attack, Alphabet Index Mapping (AIM), which achieves high success rates in bypassing safety filters ("jailbreaking"). AIM encodes prompts by converting characters to their alphabet indices, maximizing semantic dissimilarity while maintaining straightforward decoding instructions. This allows malicious prompts to evade detection based on semantic similarity, even when the LLM correctly decodes the intent.

A novel black-box attack, dubbed BitBypass, exploits the vulnerability of aligned LLMs by camouflaging harmful prompts using hyphen-separated bitstreams. This bypasses safety alignment mechanisms by transforming sensitive words into their bitstream representations and replacing them with placeholders, in conjunction with a specially crafted system prompt that instructs the LLM to convert the bitstream back to text and respond as if given the original harmful prompt.

Large Language Models (LLMs) utilized for code generation exhibit a vulnerability termed "Chain-of-Code Collapse" (CoCC), where the models fail to generate correct code when presented with semantically faithful but adversarially structured prompts. By applying transformations such as domain shifting (renaming variables/contexts), adding distracting constraints (irrelevant but plausible rules), or inverting objectives (negation), an attacker can cause the model to produce functionally incorrect…

Mainstream Large Language Models (LLMs), including DeepSeek-R1, GPT-4o, Llama-3.3-70B-Instruct, and Qwen-Coder, are vulnerable to black-box jailbreak attacks that bypass safety alignment mechanisms to generate functional malicious code. The vulnerability is exploited through specific prompt engineering techniques, most notably "Benign Expression" (substituting malicious keywords with harmless synonyms) and "DRA" (Decomposed Requirement Attack), which conceal malicious intent within seemingly…