Language Model Security Database

A hybrid jailbreak attack, combining gradient-guided token optimization (GCG) with iterative prompt refinement (PAIR or WordGame+), bypasses LLM safety mechanisms resulting in the generation of disallowed content. The hybrid approach leverages the strengths of both techniques, circumventing defenses effective against single-mode attacks. Specifically, the combination of semantically crafted prompts and strategically placed adversarial tokens confuse and overwhelm existing defenses.

The MIST attack exploits a vulnerability in black-box large language models (LLMs) allowing iterative semantic tuning of prompts to elicit harmful responses. The attack leverages synonym substitution and optimization strategies to bypass safety mechanisms without requiring access to the model's internal parameters or weights. The vulnerability lies in the susceptibility of the LLM to semantically similar prompts that trigger unsafe outputs.

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.

Large Language Model (LLM) agents capable of invoking external APIs are vulnerable to intent integrity violations. When an agent receives natural language instructions that are ambiguous, underspecified, or contain values not supported by the underlying API schema, the agent frequently fails to preserve user intent. Instead of rejecting the request or asking for clarification, the model may hallucinate parameter values, map unsupported requests to unsafe defaults, or execute actions on…

A vulnerability exists in the safety alignment mechanisms of Large Language Models (LLMs) (including GPT-4, Claude 3, Gemini, and Qwen families) leading to "Implicit Harm." Unlike traditional jailbreaks that use overtly harmful queries, this vulnerability allows remote attackers to coerce the model into providing factually incorrect, plausible, and dangerous responses to benign-looking inputs. By employing "JailFlip" techniques—specifically constructed affirmative-type or denial-type queries…

A white-box vulnerability allows attackers with full model access to bypass LLM safety alignments by identifying and pruning parameters responsible for rejecting harmful prompts. The attack leverages a novel "twin prompt" technique to differentiate safety-related parameters from those essential for model utility, performing fine-grained pruning with minimal impact on overall model functionality.

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…

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) 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 adaptive jailbreaking attacks that exploit their semantic comprehension capabilities. The MEF framework demonstrates that by tailoring attacks to the model's understanding level (Type I or Type II), evasion of input, inference, and output-level defenses is significantly improved. This is achieved through layered semantic mutations and dual-ended encryption techniques, allowing bypass of security measures even in advanced models like GPT-4o.