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A curated map of recent LLM and MLLM safety guardrail resources: benchmarks, datasets, guard models, attack methods, evaluation suites, and deployment frameworks. The list is organized by how a practitioner usually works: choose a modality, pick datasets, select guard baselines, test against attacks, and wire the guard into a runtime system.
Contributions are welcome. If you find missing papers, datasets, models, or tools, please open an issue or submit a pull request.
Sorting: Tables with
Venue & Yearare sorted by year in descending order within each category.
Modality Legend:
[T]Text |[I]Image |[V]Video |[M]Multi-modal |[D]Dialogue
| Benchmark | Paper | Venue & Year | Modality | Description / Highlights | Links |
|---|---|---|---|---|---|
| SafePyramid | SafePyramid: A Hierarchical Benchmark for In-context Policy Guardrailing | arXiv 2026 | [T],[D] |
Hierarchical in-context policy guardrailing benchmark with 1K multi-turn conversations, 3K application-specific policies, and 61K+ natural-language rules across three difficulty levels | - |
| Gate AI Eval Harness | Gate AI: LLM Security Benchmark Evaluation Methodology and Results | arXiv 2026 | [T] |
Evaluation harness for prompt-injection and jailbreak detectors across 16 public benchmarks with global operating-point selection | - |
| StreamSafe | SentGuard: Sentence-Level Streaming Guardrails for Large Language Models | arXiv 2026 | [T] |
Sentence-level streaming safety benchmark with structured annotations across 8 harm categories and risk evolution over reasoning/response segments | - |
| GuardZoo | Triaging Threats to Specialized Guardrails | arXiv 2026 | [T] |
Human-annotated guardrail benchmark with 32K+ samples across 15 unsafe categories | - |
| PII-Bench | GLiNER Guard: Unified Encoder Family for Production LLM Safety and Privacy | arXiv 2026 | [T] |
Span-level PII benchmark for end-to-end privacy detection in guardrail pipelines | Dataset |
| ATBench | AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security | arXiv 2026 | [T] |
Fine-grained agentic safety benchmark with risk source, failure mode, and consequence taxonomy | Project |
| ExpGuardMix | ExpGuard: LLM Content Moderation in Specialized Domains | ICLR 2026 | [T] |
Domain-specific moderation data for finance, medical, and legal safety | Dataset |
| Aegis2 | AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails | NAACL 2025 | [T] |
Diverse safety data | Dataset |
| Nemotron Safety Guard Dataset v3 | Llama Nemotron Safety Guard: A Multilingual Input-Output Safety Model and Reasoning Dataset | arXiv 2025 | [T] |
Multilingual guard training data, 500K+ samples across 12 languages | Dataset |
| PolyGuard | PolyGuard: Towards Detecting Unsafe Multilingual LLM Prompts | COLM 2025 | [T] |
Multilingual safety | Dataset |
| SocialHarmBench | SocialHarmBench: Revealing LLM Vulnerabilities to Socially Harmful Requests | arXiv 2025 | [T] |
Sociopolitical harms (585 prompts, 34 countries) | Dataset |
| AgentHarm | AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents | ICLR 2025 | [T] |
LLM agent harmfulness (260 behaviors) | Dataset |
| Pre-Exec Bench | Building a Foundational Guardrail for General Agentic Systems via Synthetic Data | arXiv 2025 | [T] |
Pre-execution agent safety benchmark for detection, categorization, explanation, and cross-planner generalization | - |
| HarmBench | HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal | ICML 2024 | [T] |
Automated red teaming benchmark | Dataset |
| JailbreakBench | JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models | NeurIPS 2024 | [T] |
Jailbreak robustness evaluation | Dataset |
| WildChat | WildChat: 1M ChatGPT Interaction Logs in the Wild | ICLR 2024 | [T] |
Real-world jailbreak dataset (1M conversations) | Dataset |
| LMSYS-Chat-1M | LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset | ICLR 2024 | [T] |
Chatbot arena dataset | Dataset |
| SafetyBench | SafetyBench: Evaluating the Safety of Large Language Models | ACL 2024 | [T] |
Comprehensive evaluation (11K prompts) | Dataset |
| XSTest | XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models | NAACL 2024 | [T] |
Refusal on safe prompts (200+) | Dataset |
| SafeRLHF | Safe RLHF: Safe Reinforcement Learning from Human Feedback | ICLR 2024 | [T] |
Multi-level alignment | Dataset |
| WildGuardMix | WildGuard: Open One-stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs | NeurIPS 2024 | [T] |
Mixture of safety data | Dataset |
| StrongREJECT | StrongREJECT: A Strong Reject for Empty Jailbreaks | NeurIPS 2024 | [T] |
Jailbreak detection (313 prompts) | Dataset |
| Do-Not-Answer | Do-Not-Answer: A Dataset for Evaluating Safeguards in LLMs | ACL 2024 | [T] |
Evaluation of refusals | Dataset |
| Jailjudge | JAILJUDGE: A Comprehensive Jailbreak Judge Benchmark with Multi-Agent Enhanced Explanation Evaluation Framework | arXiv 2024 | [T] |
Comprehensive jailbreak judge | Dataset |
| ToxicChat | ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation | EMNLP 2023 | [T] |
Real-world toxicity detection | Dataset |
| BeaverTails | BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset | NeurIPS 2023 | [T] |
Safety pairs (360K pairs) | Dataset |
| Benchmark | Paper | Venue & Year | Modality | Description / Highlights | Links |
|---|---|---|---|---|---|
| EgoSafetyBench | EgoSafetyBench: A Diagnostic Egocentric Video Benchmark for Evaluating Embodied VLMs as Runtime Safety Guards | arXiv 2026 | [V],[M] |
Egocentric video benchmark with 1,200 robot-view scenarios and half-second annotations for streaming embodied VLM safety guards | - |
| SingGuard-Bench | SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning | arXiv 2026 | [M],[D] |
Multimodal guardrail benchmark with 56K+ examples, 80+ fine-grained risk types, dynamic-rule evaluation, and cross-modal joint-risk cases | Code |
| MTMCS-Bench | MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn Dialogues | arXiv 2026 | [M],[D] |
Multi-turn multimodal contextual safety benchmark with escalation and context-switch risk settings | Dataset Code |
| VLSU | VLSU: Mapping the Limits of Joint Multimodal Understanding for AI Safety | ICLR 2026 | [M] |
Vision Language Safety Understanding (8K+ pairs) | - |
| SafeEditBench | Towards Policy-Adaptive Image Guardrail: Benchmark and Method | arXiv 2026 | [I],[M] |
Cross-policy image guardrail benchmark with policy-aligned safe/unsafe image pairs | Dataset |
| SafeVision | SafeVision: Efficient Image Guardrail with Robust Policy Adherence and Explainability | arXiv 2025 | [M] |
Multimodal safety evaluation | - |
| SafeWatch | SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations | ICLR 2025 | [V] |
Video guardrail (2M+ videos) | - |
| VisionHarm | SafeVision: Efficient Image Guardrail with Robust Policy Adherence and Explainability | arXiv 2025 | [I] |
Image safety dataset used by SafeVision | - |
| UnsafeBench | UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images | CCS 2025 | [I] |
Image classifier safety | - |
| MSTS | MSTS: A Multimodal Safety Test Suite for Vision-Language Models | arXiv 2025 | [M] |
Multimodal Safety Test Suite | - |
| MSSBench | Multimodal Situational Safety | ICLR 2025 | [M] |
Multimodal situational safety | - |
| Video-SafetyBench | Video-SafetyBench: A Benchmark for Safety Evaluation of Video Large Language Models | NeurIPS 2025 | [V] |
Video safety benchmark | - |
| SafeMT | SafeMT: Multi-turn Safety for Multimodal Language Models | arXiv 2025 | [M],[D] |
Multi-turn multimodal safety | - |
| BeaverTails-V | Safe RLHF-V | arXiv 2025 | [I],[T] |
Multimodal safety data across visual/text harm categories | Dataset |
| MM-SafetyBench | MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models | ECCV 2024 | [M] |
Multimodal safety benchmark | - |
| JailbreakV-28K | JailbreakV-28K: A Benchmark for Assessing the Robustness of Multimodal Large Language Models against Jailbreak Attacks | arXiv 2024 | [M] |
28K jailbreak pairs | - |
| Benchmark | Paper | Venue & Year | Modality | Description / Highlights | Links |
|---|---|---|---|---|---|
| SafeDialBench | SafeDialBench: A Fine-Grained Safety Evaluation Benchmark for Large Language Models in Multi-Turn Dialogues with Diverse Jailbreak Attacks | arXiv 2025 | [D] |
Multi-turn jailbreak dialogues | - |
| CoSafe | CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference | EMNLP 2024 | [D] |
Multi-turn dialogue safety | - |
| Model | Paper | Venue & Year | Modality | Description / Highlights | Links |
|---|---|---|---|---|---|
| DT-Guard | DT-Guard: Intent-Driven Reasoning-Active Training for Reasoning-Free LLM Safety Guardrail | arXiv 2026 | [T] |
Reasoning-active training and reasoning-free inference guardrail that internalizes intent, category, and safety labels for low-latency moderation | - |
| HaloGuard 1.0 | HaloGuard 1.0: An Open Weights Constitutional Classifier for Multilingual AI Safety | arXiv 2026 | [T] |
Open-weights constitutional classifier for multilingual prompt safety across 46 policies and 46 languages | HF |
| kNNGuard | kNNGuard: Turning LLM Hidden Activations into a Training-Free Configurable Guardrail | arXiv 2026 | [T] |
Training-free configurable guardrail using hidden activations and small safe/unsafe prompt banks for fast domain adaptation | - |
| LeanGuard | Do Safety Guardrails Need to Reason? LeanGuard: A Fast and Light Approach for Robust Moderation | arXiv 2026 | [T] |
Lightweight label-only encoder guardrail that questions CoT necessity and reports ~100x lower inference compute than reasoning guards | Code |
| TRIAD | From Risk Classification to Action Plan Remediation: A Guardrail Feedback Driven Framework for LLM Agents | arXiv 2026 | [T] |
Guardrail-integrated agent framework that turns safety feedback into plan remediation instead of only allow/block decisions | Code |
| IndicGuard | IndicGuard: A Multilingual Safety Guard Model and Dataset for Indic Languages | arXiv 2026 | [T] |
Multilingual safety guard model and culturally nuanced dataset for ten major Indic languages, localized harm categories, and adversarial jailbreaks | - |
| Membrane | Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense | arXiv 2026 | [T] |
Self-evolving guardrail using Contrastive Safety Memory for adaptive jailbreak and agent-safety defense without retraining | - |
| GuardNet | GuardNet: Ensemble Strategies of Shallow Neural Networks for Robust Prompt Injection and Jailbreak Detection | arXiv 2026 | [T] |
Lightweight BiLSTM ensemble guardrail for low-latency prompt-injection and jailbreak detection | - |
| SentGuard | SentGuard: Sentence-Level Streaming Guardrails for Large Language Models | arXiv 2026 | [T] |
Parallel sentence-level streaming guardrail that buffers generated text and moderates complete sentence chunks before release | - |
| BraveGuard | BraveGuard: From Open-World Threats to Safer Computer-Use Agents | arXiv 2026 | [T] |
Self-evolving trajectory-level guard training from open-world threat signals for computer-use agents | HF |
| ConsisGuard | ConsisGuard: Aligning Safety Deliberation with Policy Enforcement in LLM Guardrails | arXiv 2026 | [T] |
Consistency-aware reasoning guardrail that aligns policy-grounded deliberation with final safety decisions | - |
| RouteGuard | Triaging Threats to Specialized Guardrails | arXiv 2026 | [T] |
Router-expert guardrail framework that dispatches conversations to specialized threat-domain experts | - |
| CoLaGuard | Robust and Efficient Guardrails with Latent Reasoning | arXiv 2026 | [T] |
Latent-reasoning guardrail that internalizes safety rationales for lower-latency prompt and response moderation | - |
| GLiNER Guard | GLiNER Guard: Unified Encoder Family for Production LLM Safety and Privacy | arXiv 2026 | [T] |
Unified encoder family for safety moderation, PII detection, and prompt attack detection in one forward pass | HF Dataset |
| GLiGuard | GLiGuard: Schema-Conditioned Classification for LLM Safeguard | arXiv 2026 | [T] |
Compact schema-conditioned bidirectional encoder for prompt safety, response safety, refusals, harm categories, and jailbreak strategies | HF Code |
| FlexGuard | FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation | ACL 2026 | [T] |
Continuous risk scoring | HF Code |
| SafeDream | SafeDream: Safety World Model for Proactive Early Jailbreak Detection | arXiv 2026 | [T] |
Safety alignment via dream methodology | - |
| MOSAIC | MOSAIC: Composable Safety Alignment with Modular Control Tokens | arXiv 2026 | [T] |
Multi-dimensional safety analysis | - |
| YuFeng-XGuard | YuFeng-XGuard: A Reasoning-Centric, Interpretable, and Flexible Guardrail Model for Large Language Models | arXiv 2026 | [T] |
Tiered inference strategy with dynamic policy adjustments | HF |
| GaaA | Guardian-as-an-Advisor: Advancing Next-Generation Guardian Models for Trustworthy LLMs | arXiv 2026 | [T] |
Soft-gating pipeline where the guardrail acts as an advisor | - |
| LEG | A Lightweight Explainable Guardrail for Prompt Safety | ACL 2026 | [T] |
Modular external guardrail providing interpretable explanations | - |
| BARRED | BARRED: Synthetic Training of Custom Policy Guardrails via Asymmetric Debate | arXiv 2026 | [T] |
High-fidelity synthetic training data via multi-agent debate | - |
| SafeHarbor | SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety | ICML 2026 | [T] |
Training-free hierarchical memory-augmented guardrail for LLM agents with dynamic rule injection and self-evolving memory structure | Code |
| ToolSafe / TS-Guard | ToolSafe: Enhancing Tool Invocation Safety of LLM-based agents via Proactive Step-level Guardrail and Feedback | arXiv 2026 | [T] |
Step-level proactive guardrail for unsafe tool invocation in LLM agents | Code |
| SafePred | SafePred: A Predictive Guardrail for Computer-Using Agents via World Models | arXiv 2026 | [T] |
Predictive guardrail for computer-using agents with short- and long-term risk prediction | Code |
| SIREN | LLM Safety From Within: Detecting Harmful Content with Internal Representations | arXiv 2026 | [T] |
Lightweight harmfulness detector using safety neurons and adaptive layer-weighted internal representations | HF Code |
| AgentDoG | AgentDoG: A Diagnostic Guardrail Framework for AI Agent Safety and Security | arXiv 2026 | [T] |
Diagnostic guardrail for agent trajectories with fine-grained risk taxonomy and root-cause explanations | Project |
| ExpGuard | ExpGuard: LLM Content Moderation in Specialized Domains | ICLR 2026 | [T] |
Specialized guardrail for harmful prompts and responses in finance, medical, and legal domains | Code Dataset Models |
| Llama Guard 4 | - | Meta 2025 | [M] |
Multimodal safety model for image and text moderation | HF |
| Prompt Guard 2 | - | Meta 2025 | [T] |
Malicious prompt injection detection | HF |
| Nemotron Safety Guard | - | NVIDIA 2025 | [T] |
Multilingual content safety guard trained with Nemotron/Aegis data | HF |
| Qwen3Guard-Gen | Qwen3Guard Technical Report | arXiv 2025 | [T] |
Generative guard with safety levels, categories, multilingual support | HF 0.6B HF 4B HF 8B Code |
| Qwen3Guard-Stream | Qwen3Guard Technical Report | arXiv 2025 | [T] |
Token-level streaming safety monitoring for incremental generation | HF 0.6B HF 4B HF 8B Code |
| Stable Guard | - | Stability AI 2025 | [T] |
Stability AI's safety model | HF |
| GuardReasoner | GuardReasoner: Towards Reasoning-based LLM Safeguards | arXiv 2025 | [T] |
Reasoning-based safeguard framework | Code |
| C-SafeGen | C-SafeGen: Certified Safe LLM Generation with Claim-Based Streaming Guardrails | NeurIPS 2025 | [T] |
Certified safe generation via claim-based streaming | - |
| R2-Guard | R2-Guard: Robust Reasoning Enabled LLM Guardrail via Knowledge-Enhanced Logical Reasoning | ICLR 2025 | [T] |
Robust reasoning guardrail using prob-graphical models | - |
| Safiron | Building a Foundational Guardrail for General Agentic Systems via Synthetic Data | arXiv 2025 | [T] |
Foundational pre-execution guardrail for general agentic systems trained with synthetic trajectories | - |
| AGrail | AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection | ACL 2025 | [T] |
Lifelong agent guardrail with adaptive safety check generation and test-time refinement | Code |
| RoboGuard | Safety Guardrails for LLM-Enabled Robots | arXiv 2025 | [T] |
Two-stage guardrail for robot plans using grounded safety rules and temporal logic synthesis | Project |
| ShieldAgent | ShieldAgent: Shielding Agents via Verifiable Safety Policy Reasoning | arXiv 2025 | [T] |
Guardrail agent using verifiable policy reasoning over agent action trajectories | Project |
| Llama Guard 3 | - | Meta Connect 2024 | [M] |
Multimodal input/output guard | HF |
| Llama Guard 2 | - | Meta 2024 | [T] |
Enhanced version | HF |
| ShieldGemma | ShieldGemma: Generative AI Content Moderation Based on Gemma | arXiv 2024 | [T] |
Google's safety filter | HF |
| WildGuard | WildGuard: Open One-Stop Moderation Tools for Safety Risks, Jailbreaks, and Refusals of LLMs | NeurIPS 2024 | [T] |
One-stop moderation tool | HF Code |
| Aegis | AEGIS: Online Adaptive AI Content Safety Moderation with Ensemble of LLM Experts | arXiv 2024 | [T] |
Online adaptive content safety | HF |
| Shield | - | NVIDIA 2024 | [T] |
Safety description guided generation | HF Code |
| Granite Guardian | - | IBM 2024 | [T] |
Specialized open-source safety models | HF Code |
| Llama Guard | Llama Guard: Safeguarding Large Language Models | arXiv 2023 | [T] |
Pioneer guard model | HF Code |
| WildGuard Lite | - | - | [T] |
Lightweight moderation model | HF |
| Beaver | - | PKU | [T] |
Open source safety alignment | HF Code |
| GPT-4 Content Mod. | - | OpenAI | [T] |
Built-in content moderation | API |
| Claude 3 Haiku | - | Anthropic | [T] |
Constitutional AI based safety | API |
| Gemini Safety | - | [T] |
Built-in safety filters | API | |
| Azure Content Safety | - | Microsoft | [T],[I] |
Enterprise content moderation | API |
| Model | Paper | Venue & Year | Modality | Description / Highlights | Links |
|---|---|---|---|---|---|
| SingGuard | SingGuard: A Policy-Adaptive Multimodal LLM Guardrail with Dynamic Reasoning | arXiv 2026 | [M],[D] |
Policy-adaptive multimodal guardrail family with runtime policy input, rule-level triggering, and fast/hybrid/slow reasoning regimes | Code |
| GuardReasoner-Omni | GuardReasoner-Omni: A Reasoning-based Multi-modal Guardrail for Text, Image, Video, and Audio | arXiv 2026 | [M] |
Omni-modal reasoning guardrail for text, image, video, and audio moderation with SFT and RL training | HF 3B HF 7B |
| SafeLens | SafeLens: Deliberate and Efficient Video Guardrails with Fast-and-Slow Screening | arXiv 2026 | [V] |
Video guardrail framework with fast-and-slow screening, influence-filtered SafeWatch data, and test-time reasoning traces | - |
| VLSU | VLSU: Mapping the Limits of Joint Multimodal Understanding for AI Safety | ICLR 2026 | [M] |
Vision Language Safety Understanding (8K+ pairs) | Code |
| SafeGuard-VL | Towards Policy-Adaptive Image Guardrail: Benchmark and Method | arXiv 2026 | [I],[M] |
Policy-aware visual safety guardrail trained with RLVR/GRPO for dynamic safety policies | HF Dataset |
| HomeGuard | HomeGuard: VLM-based Embodied Safeguard for Identifying Contextual Risk in Household Task | arXiv 2026 | [M] |
Embodied VLM safeguard for contextual household risk with grounded visual anchors | Code |
| Pragma-VL | Pragma-VL: Towards a Pragmatic Arbitration of Safety and Helpfulness in MLLMs | ICLR 2026 | [M] |
End-to-end MLLM safety-helpfulness arbitration with risk-aware visual perception | Code Dataset |
| UniMod | From Sparse Decisions to Dense Reasoning: A Multi-attribute Trajectory Paradigm for Multimodal Moderation | arXiv 2026 | [M] |
Multimodal moderation via dense reasoning trajectories, evidence grounding, risk mapping, and policy decisions | - |
| SafeVision | SafeVision: Efficient Image Guardrail with Robust Policy Adherence and Explainability | ICLR 2025 | [M] |
Multimodal safety evaluation | HF |
| SaFeR-VLM | SaFeR-VLM: Toward Safety-aware Fine-grained Reasoning in Multimodal Models | arXiv 2025 | [M] |
Safety-aware multimodal reasoning framework using safety rollouts, structured rewards, and GRPO | Code |
| SafeWatch | SafeWatch: An Efficient Safety-Policy Following Video Guardrail Model with Transparent Explanations | ICLR 2025 | [V] |
Video guardrail model (2M+ videos) | HF Code |
| ShieldGemma 2 | ShieldGemma 2: Robust and Tractable Image Content Moderation | arXiv 2025 | [I],[M] |
Open image moderation model for sexually explicit, violence/gore, and dangerous content | HF |
| Llama Guard 4 | - | Meta 2025 | [M] |
Multimodal text/image guard for prompt and response moderation | HF |
| LlavaGuard | LlavaGuard: An Open VLM-based Framework for Safeguarding Vision Datasets and Models | ICML 2025 | [I],[M] |
VLM safeguard with safety rating, category, and rationale annotations | Code |
| GuardReasoner-VL | GuardReasoner-VL: Safeguarding VLMs via Reinforced Reasoning | NeurIPS 2025 | [M] |
Reinforced reasoning-based guard model for VLM safety | Code |
| OMNIGUARD | OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Modalities | EMNLP 2025 | [M] |
Efficient safety moderation across languages and modalities using internal representations | Paper |
| VisionHarm | SafeVision: Efficient Image Guardrail with Robust Policy Adherence and Explainability | ICLR 2025 | [I] |
Image safety detection | - |
| LLaVAShield | LLaVAShield: Safeguarding Multimodal Multi-Turn Dialogues in Vision-Language Models | arXiv 2025 | [M],[D] |
Safeguarding multimodal multi-turn dialogues | HF |
| UnsafeBench | UnsafeBench: Benchmarking Image Safety Classifiers on Real-World and AI-Generated Images | CCS 2025 | [I] |
Image classifier safety evaluation | HF Code |
| MSTS | MSTS: A Multimodal Safety Test Suite for Vision-Language Models | arXiv 2025 | [M] |
Multimodal Safety Test Suite | HF Code |
| MSSBench | Multimodal Situational Safety | ICLR 2025 | [M] |
Multimodal situational safety | Code |
| Llama Guard 3 Vision | Llama Guard 3 Vision: Safeguarding Human-AI Image Understanding Conversations | arXiv 2024 | [M] |
Multimodal input/output guard | HF Code |
| MM-SafetyBench | MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models | ECCV 2024 | [M] |
Multimodal safety benchmark | HF Code |
White-box: Requires model weights/gradients | Black-box: API-only access
| Attack | Paper | Venue & Year | Modality | Description / Highlights | Links |
|---|---|---|---|---|---|
| Guardrail Recon | Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring | arXiv 2026 | [T] |
Black-box reconnaissance method that distinguishes guardrail blocks from model refusals using HTTP, lexical, and timing signals | - |
| Guardrail DoS | From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails | arXiv 2026 | [T] |
Availability attack that traps reasoning-based agent guardrails in extended reasoning loops, causing large token and latency amplification | - |
| Posterior Attack | Safety Paradox: How Enhanced Safety Awareness Leaves LLMs Vulnerable to Posterior Attack | arXiv 2026 | [T] |
Single-query jailbreak that exploits models' internal safety awareness to bypass guardrails | - |
| Prompt Overflow | Prompt Overflow: What the Guardrail Inspects Is Not What the Model Infers | arXiv 2026 | [T] |
Long-context attack that exploits mismatch between guardrail inspection windows and downstream LLM inference windows | - |
| JRS-Rem | Understanding and Defending VLM Jailbreaks via Jailbreak-Related Representation Shift | arXiv 2026 | [M] |
Explores representation shift in VLM jailbreaks & defense | - |
| ALERT | ALERT: Zero-shot LLM Jailbreak Detection via Internal Discrepancy Amplification | arXiv 2026 | [T] |
Zero-shot detection via internal feature discrepancies | - |
| Echo Chamber | The Echo Chamber Multi-Turn LLM Jailbreak | arXiv 2026 | [T],[D] |
Multi-turn gradual escalation attack | - |
| AMIS | Align to Misalign: Automatic LLM Jailbreak with Meta-Optimized LLM Judges | ICLR 2026 | [T] |
Meta-optimization evolving prompts and scoring | - |
| Odysseus | Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganography | NDSS 2026 | [M] |
Hides malicious intent via dual steganography in images/audio | - |
| PAIR | Jailbreaking Black Box Large Language Models in Twenty Queries | SaTML 2025 | [T] |
Iterative LLM-based refinement | Code |
| FigStep | FigStep: Jailbreaking Large Vision-Language Models via Typographic Visual Prompts | AAAI 2025 | [M] |
VLM multimodal jailbreak | - |
| Crescendo | Great, Now Write an Article About That: The Crescendo Multi-Turn LLM Jailbreak Attack | USENIX 2025 | [T],[D] |
Multi-turn escalating attack | - |
| X-Teaming | X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents | COLM 2025 | [T],[D] |
Multi-agent coordination | - |
| FlipAttack | FlipAttack: Jailbreak LLMs via Flipping | ICML 2025 | [T] |
Left-side noise attack | Code |
| E2AT / ProEAT | E$^2$AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models | arXiv 2025 | [M] |
Adversarial training paradigm tailored for MLLMs | - |
| LogiBreak | Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression | arXiv 2025 | [T] |
Converts prompts to formal logical expressions | - |
| DeepInception | DeepInception: Hypnotize Large Language Model to Be Jailbreaker | NeurIPS 2024 | [T] |
Multi-layer role-play | Code |
| TAP | Tree of Attacks: Jailbreaking Black-Box LLMs Automatically | NeurIPS 2024 | [T] |
Tree-of-thought attack | Code |
| ArtPrompt | ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs | ACL 2024 | [T] |
ASCII art obfuscation | Code |
| SelfCipher | GPT-4 Is Too Smart To Be Safe: Stealthy Chat with LLMs via Cipher | ICLR 2024 | [T] |
Cipher encoding | Code |
| ReNeLLM | A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily | ACL 2024 | [T] |
Disguise & reconstruction | Code |
| Many-shot | Many-shot Jailbreaking | Anthropic 2024 | [T] |
Long-context few-to-many-shot jailbreaking | - |
| FuzzLLM | FuzzLLM: A Novel and Universal Fuzzing Framework for Proactively Discovering Jailbreak Vulnerabilities in Large Language Models | ICASSP 2024 | [T] |
Fuzzing-based discovery | Code |
| MasterKey | MasterKey: Automated Jailbreak Across Multiple Large Language Model Chatbots | NDSS 2024 | [T] |
Chatbot auto-jailbreak | - |
| CodeChameleon | CodeChameleon: Personalized Encryption Framework for Jailbreaking Large Language Models | ACL 2024 | [T] |
Personalized encryption | Code |
| SOP | SeqAR: Jailbreak LLMs with Sequential Auto-Generated Characters | NeurIPS W 2024 | [T] |
Social facilitation attack | Code |
| Tool | Paper | Links | Description |
|---|---|---|---|
| Garak | - | Code | LLM vulnerability scanner |
| JailbreakBench | - | Code | Robustness benchmark |
| MMRT | - | - | Multimodal multi-turn red teaming |
| Inspectus | - | - | Security evaluation |
| Aporia | - | - | LLM monitoring and safety |
| Tool | Paper | Links | Stars | Description |
|---|---|---|---|---|
| Armorer Guard | - | Code | ⭐40+ | Local Rust scanner and MCP proxy for AI-agent prompt injection, credential leaks, exfiltration, and risky tool calls |
| NeMo Guardrails | - | Code | ⭐6K+ | NVIDIA's programmable guardrails |
| LLM-Guard | - | Code | ⭐2.8K+ | Content filtering library |
| Guardrails | - | Code | ⭐6.7K+ | Policy enforcement |
| DarkMoon Privacy Gateway | - | Code | - | Self-hosted proxy and MCP host that tokenizes sensitive values into deterministic reversible placeholders before prompts reach the LLM, with an anti-exfiltration command gateway |
| Promptfoo | - | Code | ⭐20K+ | LLM testing and safety evaluation |
| gate.cat | - | Code | ⭐0 | Deterministic, fail-closed action veto that blocks irreversible shell commands (rm -rf, DROP TABLE, terraform destroy) from AI coding agents before they run, with no LLM in the veto path; runs as a Claude Code PreToolUse hook, gated shell, or local OpenAI-API proxy |
| verbatim-citation-gate | - | Code | ⭐3 | Deterministic, fail-closed citation check that verifies a quoted span literally appears in the source it was attributed to, returning found/misattributed/not_found with no LLM in the verification path; Unicode-aware normalization, usable as a pre-filter ahead of a judge model |
| Tool | Paper | Description |
|---|---|---|
| LM Eval Harness | - | Standardized evaluation |
| Confident AI | - | LLM evaluation platform |
| Giskard | - | Testing for ML models |
| Helicone | - | LLM observability |
| Guardrails Hub | - | Pre-built guardrail policies |
| Project | Paper | Description | Links | Stars |
|---|---|---|---|---|
| ArmorerLabs/Armorer-Guard | - | Local Rust scanner and MCP proxy for AI-agent security | GitHub | ⭐40+ |
| enguard-ai/awesome-ai-guardrails | - | Curated AI guardrails resources | GitHub | ⭐50+ |
| beyefendi/awesome-llm-security | - | LLM security resources | GitHub | ⭐10+ |
| yueliu1999/Awesome-Jailbreak-on-LLMs | - | Jailbreak resources | GitHub | ⭐1.3K+ |
| CryptoAILab/Awesome-LM-SSP | - | Safety security privacy | GitHub | ⭐1.9K+ |
| NVIDIA/NeMo-Guardrails | - | Programmable guardrails | GitHub | ⭐6K+ |
| laiyer-ai/LLM-Guard | - | Content filtering | GitHub | ⭐2.8K+ |
| guardrails-ai/guardrails | - | Policy enforcement | GitHub | ⭐6.7K+ |
| promptfoo/promptfoo | - | LLM testing | GitHub | ⭐20K+ |
| NVIDIA/garak | - | LLM vulnerability scanner | GitHub | ⭐7.6K+ |
| allenai/wildguard | - | Safety moderation | GitHub | ⭐100+ |
| PKU-Alignment/safe-rlhf | - | Safety alignment | GitHub | ⭐1.6K+ |
For questions or collaborations, please contact:
- Zongyi Li: lizongyi.lzy@antgroup.com
- Yichen Bai: baiyichen.byc@antgroup.com
- Liangbo He: liangbo.hlb@antgroup.com
- Jun Lan: yelan.lj@antgroup.com