Language Model Security Database

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…

Large Language Models (LLMs) deployed in automated peer review workflows are vulnerable to targeted textual adversarial attacks. By employing a technique defined as "Attack Focus Localization," an attacker can identify critical document segments via Longest Common Subsequence (LCS) matching between the original text and an initial LLM-generated review. Injecting semantic-preserving perturbations—such as character-level noise, synonym substitution (e.g., TextFooler), or stylistic transfer…

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) that utilize byte-stream parsing or structural extraction to process PDF files—specifically the OpenAI GPT and Anthropic Claude families—are vulnerable to adversarial text injection via imperceptible "phantom tokens." This vulnerability exploits the disconnect between how PDF viewers render documents for humans (visual layer) and how LLMs extract text from the PDF operator stream (data layer). Attackers can manipulate standard PDF text-showing operators (TJ and Tj)…

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.

Computer-Use Agents (CUAs) powered by Large Language Models (LLMs) operating in hybrid Web-OS environments are vulnerable to indirect prompt injection. Attackers can embed malicious natural language or code instructions within legitimate web content (e.g., social media forums, chat applications, shared cloud documents) that the agent processes during benign task execution. Due to the agent's inability to distinguish between trusted user instructions and untrusted environmental data, the CUA…

Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit the model's inherent persuasive nature. A novel attack framework, CL-GSO, decomposes jailbreak strategies into four components (Role, Content Support, Context, Communication Skills), creating a significantly expanded strategy space compared to prior methods. This expanded space allows for the generation of prompts that bypass safety protocols with a success rate exceeding 90% on models previously considered…

A vulnerability in Large Language Models (LLMs) allows adversarial prompt distillation from a large language model (LLM) to a smaller language model (SLM), enabling efficient and stealthy jailbreak attacks. The attack leverages knowledge distillation techniques, reinforcement learning, and dynamic temperature control to transfer the LLM's ability to bypass safety mechanisms to a smaller, more easily deployable SLM. This allows for lower computational cost attacks with a potentially high…