Language Model Security Database

Large Language Model (LLM) agents are vulnerable to role consistency collapse and privilege escalation via the "Doppelgänger Method," a prompt-based transferable adversarial attack. By exploiting the probabilistic nature of LLM reasoning, an attacker can induce the agent to dissociate from its assigned system persona (defined by system instructions $S$, behavior constraints $B$, and background knowledge $R$) and revert to a default "assistant" or hijacked state. This vulnerability allows…

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.

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.

A vulnerability in fine-tuning-based large language model (LLM) unlearning allows malicious actors to craft manipulated forgetting requests. By subtly increasing the frequency of common benign tokens within the forgetting data, the attacker can cause the unlearned model to exhibit unintended unlearning behaviors when these benign tokens appear in normal user prompts, leading to a degradation of model utility for legitimate users. This occurs because existing unlearning methods fail to…

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.