Language Model Security Database

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.

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…

Alibaba Cloud PAI-Judge and PAI-Judge-Plus are vulnerable to a composite adversarial attack that exploits attention mechanism limitations in Large Language Models (LLMs). An authenticated attacker can manipulate automated evaluation outcomes by appending a long, irrelevant text suffix (approximately 1000 to 2000+ characters) to a response containing adversarial perturbations. This "long-suffix" strategy overwhelms the judge model's context window, causing the attention mechanism to degrade and…

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)…

The Adaptive Greedy Binary Search (AGBS) framework exposes a vulnerability in Large Language Models (LLMs) regarding their susceptibility to semantic-preserving adversarial attacks. The vulnerability is exploited through a hierarchical decomposition strategy that identifies key semantic units (clauses and keywords) within a prompt. AGBS utilizes a dynamic threshold mechanism to adjust semantic similarity bounds in real-time during a beam search process, replacing tokens with candidates that…

Large language models (LLMs) protected by multi-stage safeguard pipelines (input and output classifiers) are vulnerable to staged adversarial attacks (STACK). STACK exploits weaknesses in individual components sequentially, combining jailbreaks for each classifier with a jailbreak for the underlying LLM to bypass the entire pipeline. Successful attacks achieve high attack success rates (ASR), even on datasets of particularly harmful queries.

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.

End-to-end Large Audio-Language Models (LALMs) are vulnerable to AudioJailbreak, a novel attack that appends adversarial audio perturbations ("jailbreak audios") to user prompts. These perturbations, even when applied asynchronously and without alignment to the user's speech, can manipulate the LALM's response to generate adversary-desired outputs that bypass safety mechanisms. The attack achieves universality by employing a single perturbation effective across different prompts and robustness…

A vulnerability exists in multiple large language and multimodal models that allows for the bypass of safety filters through the use of code-mixed prompts with phonetic perturbations. An attacker can craft a prompt in a code-mixed language (e.g., Hinglish) and apply phonetic misspellings to sensitive keywords (e.g., spelling "hate" as "haet"). This technique causes the model's tokenizer to parse the sensitive word into benign sub-tokens, preventing safety mechanisms from flagging the harmful…

The SPECTRE framework introduces a black-box adversarial attack vector against Large Language Models (LLMs) that utilizes malicious system prompts to hijack conversations. Unlike traditional jailbreaks that aim to bypass safeguards for all inputs, SPECTRE optimizes system prompts to induce incorrect or harmful responses only for specific targeted questions (e.g., "Are COVID vaccines safe?", "Who should I vote for?"), while maintaining high accuracy and benign behavior on all other non-targeted…