OmniGuard: Unified AI Safety Frameworks

• OmniGuard is a set of frameworks that enhance AI safety by integrating watermarking, harmful prompt detection, omni-modal guardrails, and SE attack protection.
• It leverages hybrid watermarking techniques and universal embedding classifiers to ensure image authenticity and cross-modal harmful input detection with human-auditable reasoning.
• Empirical results show robust performance and real-time efficiency across diverse modalities, positioning OmniGuard as a state-of-the-art solution in AI safety and forensics.

OmniGuard is the designation of several independently developed frameworks targeting safety, forensic analysis, and manipulation detection in AI systems. Approaches under the OmniGuard name span robust watermarking for AI-generated images, efficient harmful prompt detection across modalities, unified omni-modal guardrails with deliberative reasoning, and personalized real-time social engineering (SE) attack detection. While independently motivated, these systems address reliability, authenticity, and safety in modern multi-modal and multi-agent AI environments.

1. Definitional Scope and Formal Task Setting

OmniGuard refers to a set of frameworks, each built for distinct, but convergent, AI safety and forensic tasks:

• Image Integrity and Tamper Localization: OmniGuard (Zhang et al., 2 Dec 2024) incorporates hybrid watermarking for both proactive (in-band) copyright embedding and passive, blind manipulation localization in images, particularly under generative AI content (AIGC) editing.
• Efficient Cross-Modal Harmful Prompt Detection: OmniGuard (Verma et al., 29 May 2025) targets the detection of harmful user inputs across natural languages and modalities (text, image, audio), leveraging LLM and MLLM universal internal representations.
• Omni-Modal Guardrails with Reasoning: OmniGuard (Zhu et al., 2 Dec 2025) provides a unified guardrail system for AI models interfacing with text, images, video, and audio, outputting explicit policy violation explanations and supporting structured, human-auditable safety interventions.
• Personalized Social Engineering Protection: SE-OmniGuard (Kumarage et al., 18 Mar 2025) is constructed for real-time detection of multi-turn social engineering attacks in chat-based systems, integrating user personality modeling for context-sensitive alerting.

2. Unified and Modality-Specific Architectures

OmniGuard implementations differ in architectural choices, but emphasize modularity, efficiency, and robust cross-modal transfer.

• Hybrid Watermarking Pipeline (Zhang et al., 2 Dec 2024):
  • Proactive embedding utilizes an invertible network to encode content-aware localized watermarks and binary copyright bitstreams into container images.
  • Passive extraction leverages a degradation-aware tamper extractor, based on windowed transformers, to localize edits or tampering without reference to the original watermark.
• Universal Embedding Safety Classifier (Verma et al., 29 May 2025):
  • Operates by identifying a layer within LLM/MLLM backbones at which semantic content is maximally aligned across languages or modalities, as measured by U-Score.
  • A small MLP classifier is trained atop these universal embeddings. At inference, classifier heads operate on embedding vectors cached during primary forward passes, yielding significant inference speedups (≈120× vs. LLM-based guards).
• Omni-Modal Guardrails with Reasoning (Zhu et al., 2 Dec 2025):
  • Utilizes the Qwen2.5-Omni transformer backbone with modular encoders for all major modalities and multi-head attention fusion.
  • Downstream heads predict safety label (binary), violation category (multi-label), and generate natural-language critiques via autoregressive decoding, ensuring human-auditability and traceability.
• Agent-Based Personalized SE Defense (Kumarage et al., 18 Mar 2025):
  • Employs a multi-agent architecture: parallel LLM-based worker agents (for personality exploitation, SE tactic detection, and sensitive information requests) relay structured outputs to a control agent which fuses risk via a weighted scoring function and applies real-time alerting logic.

3. Algorithmic Foundations and Procedural Mechanisms

Each OmniGuard system formalizes algorithmic steps and loss functions to maximize robustness, sensitivity, and interpretability.

• Watermark Embedding and Extraction (Zhang et al., 2 Dec 2024):
  • Embedding yields a container image $I_{\text{con}}$ via $$\mathcal{E}_{\mathrm{enc}}(I_{\text{ori}}, \tilde{W}_{\text{loc}}, w_{\text{cop}})$$, with loss terms over fidelity, localized watermark recovery, GAN regularization, and perceptual similarity (LPIPS).
  • Degradation-aware extraction fuses local artifact maps $\hat{W}_{\text{loc}}$ and learned queries to predict tamper masks $\hat{M}_{\text{loc}}$ via a Swin-ViT backbone.
• Universal Embedding Selection (Verma et al., 29 May 2025):
  • U-Score establishes the universality of layer embeddings:

  $$\text{U-Score}(\ell) = \frac{1}{N}\sum_{i=1}^N \cos(\mathrm{Emb}_\ell(e_i), \mathrm{Emb}_\ell(l_i)) - \frac{1}{N(N-1)}\sum_{i \neq j} \cos(\mathrm{Emb}_\ell(e_i), \mathrm{Emb}_\ell(l_j))$$ - The classifier then optimizes standard cross-entropy with $L_2$ regularization.

• Omni-Modal Reasoning Loss (Zhu et al., 2 Dec 2025):

  • Training proceeds with joint instruction tuning over safety classification ($L_{\text{cls}}$), multi-label violation categories ($L_{\text{cat}}$), and token-level critique alignment ($L_{\text{crit}}$):

  $$L_{\text{total}} = \lambda_1 L_{\text{cls}} + \lambda_2 L_{\text{cat}} + \lambda_3 L_{\text{crit}}$$

• SE-OmniGuard Multi-Agent Aggregation (Kumarage et al., 18 Mar 2025):

  • Subscores for exploitation, strategy, and info-extraction ($s_1, s_2, s_3 \in [0,1]$) are fused:

  $$R(t) = w_1 s_1(t) + w_2 s_2(t) + w_3 s_3(t), \quad m(t) = \lceil 10 R(t) \rceil$$ - Alerts are triggered if $m(t) \geq \tau$.

4. Datasets, Metrics, and Comparative Evaluation

The breadth of OmniGuard systems entails diverse experimental setups, but all prioritize benchmarking against state-of-the-art (SOTA) and legacy baselines.

• Omni-Modal and Cross-Modal Benchmarks (Zhu et al., 2 Dec 2025, Verma et al., 29 May 2025):

  • Text: BeaverTails, ToxicChat, WildGuardMix, Aegis 2.0, OpenAI Moderation, HarmBench, RTP-LX, AyaRedTeaming, among others.
  • Images: UnsafeBench, VLGuard, MM-Vet, JailBreakV-28K, VLSafe, Hades.
  • Video: SafeSora, Video-SafetyBench.
  • Audio: MuTox-English, WildGuardMix-TTS, VoiceBench, Achilles/AIAH.
  • Cross-modal: MM-SafetyBench, FigStep, MML-SafeBench.
  • Metrics: Accuracy, F1 (matching accuracy in near-balanced datasets), PSNR, SSIM, IoU, AUC, localization and bit recovery accuracy.
• Empirical Results:
  • OmniGuard (Zhang et al., 2 Dec 2024) achieves PSNR ≈ 42.33 dB and F1 localization ≈ 0.961 for images, with bit accuracy >98% even under severe AIGC edits.
  • OmniGuard (Verma et al., 29 May 2025) improves harmful prompt classification by +11.57 pp F1 over the strongest baseline in multilingual tasks and +20.44 pp on image-based prompts, with average runtime improvement ≈120× over LlamaGuard3.
  • OmniGuard (Zhu et al., 2 Dec 2025) achieves text F1 up to 88.0% (vs. baseline 84.1%), image F1 up to 80.0% (vs. 75.2%), video F1 up to 85.7%, and cross-modal accuracy of ~86.1%.
  • SE-OmniGuard (Kumarage et al., 18 Mar 2025) outperforms baseline one-shot LLM detection (F1 ~0.82 vs. 0.52), with detection accuracy for unsuccessful/partial/full SE attacks of ~0.85, 0.75, and 0.82, respectively.

OmniGuard Variant	Primary Modality(ies)	Core Metric (Best Reported)	Benchmark/Domain
(Zhang et al., 2 Dec 2024)	Images	PSNR: 42.33 dB; F1: 0.961	AIGC-editing, watermarking
(Verma et al., 29 May 2025)	Text, Vision, Audio	F1: +11.57 pp over baseline	Multilingual, multimodal
(Zhu et al., 2 Dec 2025)	Text, Vision, Video, Audio	F1: 88.0% (text); 80.0% (image); 85.7% (video)	Omni-modal guardrails
(Kumarage et al., 18 Mar 2025)	Chat, Text	F1: 0.815	Social engineering defense

5. Generalization, Robustness, and Efficiency

OmniGuard frameworks deliver strong cross-modal, cross-domain generalization due to explicit alignment techniques, modular fusion policies, and training on large, structured datasets.

• Robustness:
  • (Zhang et al., 2 Dec 2024): Tamper localization and copyright extraction remain effective under strong JPEG, noise, and color perturbations; flexible selection of watermark templates enables broad deployment.
  • (Verma et al., 29 May 2025, Zhu et al., 2 Dec 2025): Demonstrated cross-modal transfer—seen-modality F1: 81.8%, unseen-modality F1: 79.4%; accurate harmfulness detection even in novel languages, ciphered inputs, and code-obfuscated settings.
  • (Kumarage et al., 18 Mar 2025): Detection pipeline yields interpretable evidence, resilient to adaptive attacker strategies (except fully paraphrased, non-explicit asks).
• Efficiency:
  • Reusing cached model embeddings for safety checks (vs. separate “guard” model forward passes) reduces compute cost by ~120× (Verma et al., 29 May 2025), enabling near-real-time operation for safety monitoring at scale.

6. Limitations and Prospective Research Directions

Key limitations vary by domain but collectively indicate several active areas for further investigation:

• Dependence on Model Internals: Universal embedding-based guards (Verma et al., 29 May 2025) require white-box access to LLM/MLLM internal layers, reducing applicability to proprietary APIs.
• Latency vs. Reasoning Depth: Deliberate reasoning and natural-language critiques in (Zhu et al., 2 Dec 2025) increase inference time (“slow thinking”), especially problematic in low-latency or streaming safety gate scenarios.
• Personality Modeling and Privacy: Personalized SE defense (Kumarage et al., 18 Mar 2025) presupposes known Big-Five personality vectors (Ptrait), raising privacy and consent issues in real-world integration.
• Ultra-High-Resolution and Multi-Modal Watermarking: (Zhang et al., 2 Dec 2024) highlights challenges in scaling watermark imperceptibility and extraction to gigapixel images or multi-modal domains.

Prospective pathways include:

• Incremental learning for evolving safety policies (Zhu et al., 2 Dec 2025).
• Multi-turn and streaming embedding pipelines (Verma et al., 29 May 2025).
• Diffusion-based or flow-based steganographic watermarking (Zhang et al., 2 Dec 2024).
• Real-time adaptive fusion between shallow fast guards and deep reasoning models (Zhu et al., 2 Dec 2025).
• Cross-modal watermarking and adversarial retraining for robust SE defense (Zhang et al., 2 Dec 2024, Kumarage et al., 18 Mar 2025).

7. Synthesis and Significance

OmniGuard encompasses leading paradigms for addressing the authenticity, safety, and responsible deployment of generative and conversational AI. Unifying persistent watermarking, efficient harmfulness detection, omni-modal chain-of-thought safety enforcement, and personalized SE defense demonstrates the breadth of mechanisms required for robust AI alignment and governance. Empirical results across benchmarks position OmniGuard-type systems as SOTA in their respective domains, with interpretable decision outputs and flexible cross-modal deployment (Zhang et al., 2 Dec 2024, Verma et al., 29 May 2025, Zhu et al., 2 Dec 2025, Kumarage et al., 18 Mar 2025).

These frameworks represent the convergence of proactive (e.g., watermarking, embedding design) and reactive (e.g., safety classification, deliberate reasoning) safety strategies, supporting both ex ante forensics and ex post intervention. Their continued advancement signals a trend toward unified, audit-ready, and semantically robust AI safety infrastructures.