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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

Filtered research findings

704 entries

Matches every word across titles, descriptions, sources, affected systems, and models.

Boilerplate safety tuning causes false refusals on benign requests

Published Analyzed Source: arXiv

Controlled safety-tuning experiments link boilerplate refusal statements to unnecessary refusals of benign requests. Request-specific rationales improve benign compliance, with benchmark-dependent safety tradeoffs.

Refuse without Refusal: A Structural Analysis of Safety-Tuning Responses for Reducing False Refusals in Language Models
Evaluated models: Llama 3.1 8B, Mistral 7B v0.3, Gemma 2 9B+9 more
  • Llama 3.1 8B
  • Mistral 7B v0.3
  • Gemma 2 9B
  • Qwen 2.5 7B
  • Gemma 2 2B
  • Qwen 2.5 3B
  • Llama 3.1 70B
  • Qwen 2.5 72B
  • Llama 3.1 8B Instruct
  • Mistral 7B Instruct v0.3
  • Gemma 2 9B IT
  • Qwen 2.5 7B Instruct

Simulated Multi-Turn Priming Bypasses LLM Safety Alignment

Published Analyzed Source: arXiv

The paper reports a reproducible black-box jailbreak evaluation in which an evolutionary search generates simulated, role-labeled multi-turn dialogue histories and submits them as a single request. Five semantic mutators and graded judge feedback optimize conversational priming that can induce harmful responses despite direct-request refusals. Results are paper-reported and not independently verified.

ContextualJailbreak: Evolutionary Red-Teaming via Simulated Conversational Priming
Evaluated models: Qwen 3 8B, GPT-oss 20B, GPT-oss 120B+7 more
  • Qwen 3 8B
  • GPT-oss 20B
  • GPT-oss 120B
  • Llama 3.1 70B
  • GPT-4o Mini
  • GPT-5
  • Gemini 3 Flash
  • claude-opus-4-7
  • Claude Sonnet 4.6
  • all-MiniLM-L6-v2

Semantic-Agnostic Multimodal Image Jailbreak

Published Analyzed Source: arXiv

The paper describes a specific black-box jailbreak evaluation, BVS, in which fragmented visual content is mixed with neutral imagery and paired with reconstruction-oriented text so harmful intent is only recomposed during multimodal reasoning. The authors report that this can bypass input and output safety assumptions in image-generating MLLMs. A safe defensive reproduction should use synthetic, non-harmful stand-ins for prohibited concepts, test whether fragmented cross-modal inputs are…

Beyond Visual Safety: Jailbreaking Multimodal Large Language Models for Harmful Image Generation via Semantic-Agnostic Inputs
Evaluated models: GPT-5, Gemini 1.5 Flash

Adaptive Multimodal Reasoning Jailbreaks

Published Analyzed Source: arXiv

The paper reports a black-box jailbreak evaluation in which a ReAct-style loop adaptively rewrites unsafe text prompts and selectively applies blur, DCT filtering, or recoloring to image regions identified as safety-sensitive. The combined cross-modal strategy is intended to make harmful image-text requests appear less objectionable to a vision-language model while preserving enough semantics to elicit an answer. This is a specific, security-relevant evaluation, although the reported results…

Jailbreaks on Vision Language Model via Multimodal Reasoning
Evaluated models: Gemini 2.0 Flash

PolyJailbreak Cross-Modal Safety Asymmetry

Published Analyzed Source: arXiv

The paper describes a reproducible black-box evaluation and attack framework, PolyJailbreak, for multimodal LLMs. It reports that uneven text-versus-vision safety alignment allows jointly optimized text and image inputs to bypass refusal behavior without model internals. The authors attribute this to visual alignment weakening textual refusal representations and to cross-modal fusion making harmful intent harder to separate from benign intent. These are paper-reported findings, not…

Multimodal Safety Is Asymmetric: Cross-Modal Exploits Unlock Black-Box MLLMs Jailbreaks
Evaluated models: LLaVA 1.5 7B, LLaVA 1.6 7B, Qwen-2.5-VL (7B)+5 more
  • LLaVA 1.5 7B
  • LLaVA 1.6 7B
  • Qwen-2.5-VL (7B)
  • Llama 3.2 11B Vision
  • GPT-4o
  • GPT-4.1
  • Gemini 2.5 Flash
  • Claude 3.7 Sonnet

Multilingual Flowchart Jailbreaks in Vision-Language Models

Published Analyzed Source: arXiv

MLingualFC is a reproducible black-box safety evaluation showing that harmful instructions rendered as multilingual flowchart images can bypass vision-language model safeguards more often than equivalent text-only inputs. The paper evaluates horizontal, vertical, and tortuous layouts across English, Hindi, Punjabi, Spanish, Romanian, and German. Reported results vary substantially by language, script, layout, and model; these are paper-reported measurements, not independently verified…

MLingualFC: Evaluating Jailbreak Vulnerabilities in Multilingual Vision-Language Models
Evaluated models: Qwen 2.5 VL 3B Instruct, Gemma-4-E4B-it, Pangea-7B

Visual-Modality Jailbreaks Bypass VLM Safety Alignment

Published Analyzed Source: arXiv

The paper reports a reproducible black-box evaluation showing that vision-language models can recover prohibited intent encoded or implied through ostensibly benign visual inputs. Four tested families—visual ciphers, object replacement, text replacement, and analogy riddles—expose a cross-modality alignment gap: safeguards effective for explicit text may not reliably apply after harmful semantics are reconstructed from images. These are paper-reported results, not independently verified…

Jailbreaking Vision-Language Models Through the Visual Modality
Evaluated models: GPT-5.2, Claude Haiku 4.5, Gemini 3 Flash+3 more
  • GPT-5.2
  • Claude Haiku 4.5
  • Gemini 3 Flash
  • Gemini 3.1 Pro
  • Qwen3-VL-235B
  • Qwen3-VL-32B

Jailbreaks Causally Suppress Refusal Representations

Published Analyzed Source: arXiv

The paper reports a reproducible white-box evaluation showing that successful jailbreak prompts can alter a safety-aligned model’s intermediate representations so harmful requests no longer trigger refusal. Its LOCA method identifies small, token-specific residual-stream changes that restore refusal on individual successful jailbreaks, providing causal evidence that jailbreak success can depend on suppressing harmfulness/refusal concepts or strengthening seemingly harmless continuation…

Minimal, Local, Causal Explanations for Jailbreak Success in Large Language Models
Evaluated models: Gemma 2 2B IT, Llama 3.1 8B Instruct

Semantic-Bridging Jailbreaks Reroute Llama-2 Safety Pathways

Published Analyzed Source: arXiv

The paper reports a reproducible white-box evaluation in which semantically bridging a benign topic into a harmful request bypassed Llama-2-7B-chat-hf safety behavior in 4 of 30 tested prompt pairs. Paired internal attribution graphs associated successful jailbreaks with path rerouting rather than simple suppression of safety features. This is a paper-reported result, not independently verified here. Defensive reproduction should use the paper’s supplied dataset and code in an isolated…

Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
Evaluated models: Llama 2 7B Chat

Attention-Head Suppression Bypasses LLM Refusal

Published Analyzed Source: arXiv

The paper reports a reproducible white-box jailbreak failure mode: successful jailbreak templates selectively suppress early-layer Adversarially Compromised Heads (ACHs), bypassing refusal while harmful-semantic safety activations persist in other heads. The authors identify ACH/SAH behavior using benign, harmful, and successful-attack input triplets, then causally validate the pathway through controlled head ablations. Safe defensive reproduction should use the paper’s released evaluation…

Robust Harmful Features Under Jailbreak Attacks: Mechanistic Evidence from Attention Head Specialization in Large Language Models
Evaluated models: Llama 3 8B Instruct, Llama 2 7B Chat, Llama 3 70B Instruct

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.