Language Model Security Database

Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) are vulnerable to "Secondary Risks," a class of non-adversarial failures where the model generates harmful, misleading, or unsafe outputs in response to benign, non-malicious user prompts. Unlike jailbreaks which require adversarial inputs, secondary risks arise from imperfect generalization and alignment failures during standard interactions. This vulnerability manifests primarily in two primitives: 1. Excessive…

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) 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…

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…

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…

Large Language Models (LLMs) aligned via techniques such as Reinforcement Learning with Human Feedback (RLHF) or Direct Preference Optimization (DPO) contain a vulnerability in how safety features are encoded within the model parameters. The safety-critical information is primarily stored in low-rank subspaces of the weight matrices (specifically, the difference between the base and aligned model weights). These low-rank subspaces are highly sensitive to parameter updates. Consequently…

Multiple Large Language Models (LLMs), including GPT-4o, Claude 3.5 Sonnet, Gemini 2.5 Flash, Gemma-2 27B, Gemma-3, and Mistral-Small-24B, exhibit a vulnerability where safety guardrails against demographic bias are bypassed through realistic contextual prompting. While prompt-based mitigations effectively suppress bias in simplified, controlled benchmarks, the introduction of realistic hiring contexts—specifically the combination of company culture descriptions (sourced from public career…

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.

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…