Input/Output Moderation in AI Systems

• Input/Output Moderation is an automated framework that detects and controls harmful or policy-violating content in AI systems using semantic classifiers and explicit harm taxonomies.
• Modern systems combine pre-inference input filtering with post-inference output moderation, achieving up to 9× reduction in jailbreak attack success rates and enhanced robustness.
• State-of-the-art implementations leverage transformer models, synthetic data, and human-in-the-loop systems to deliver scalable, low-latency moderation across text, image, and audio modalities.

Input/Output Moderation refers to the automated detection and control of harmful, illegal, or policy-violating content at both the ingress (input) and egress (output) boundaries of AI systems, most notably in LLMs, generative vision models, audio pipelines, and distributed infrastructure. Modern approaches integrate semantic classifiers, fine-grained taxonomies of “harms,” adversarial robustness techniques, and, for critical deployments, cryptographic or circuit-breaking mechanisms that provide assurances over both user-contributed and AI-generated material. Input/Output moderation frameworks are central to mitigating safety, fairness, and abuse risks in interactive AI systems deployed at scale.

1. Harm Taxonomies and Policy Foundations

Robust Input/Output moderation is grounded in explicit taxonomies of disallowed or high-risk content, typically derived from legal, regulatory, or developer policy requirements. Taxonomies are designed both for general LLMs and specialized domains:

• LLM Moderation Taxonomies: Llama Guard employs a compact, developer-centric set: Violence & Hate, Sexual Content, Guns & Illegal Weapons, and Regulated Substances, each concretely defined for prompt and response classification (Inan et al., 2023).
• Embodied Agent Moderation: An expanded seven-category schema targets embodied AI safety, including Physical Harm, Dangerous Weapons, Privacy Invasion, Property Damage, Illicit Behavior, and Self-Harm/Suicide Assistance (Wang et al., 22 Apr 2025).
• Vision-Language and Image Generators: ShieldGemma 2 and related frameworks decompose harms into Sexually Explicit, Violence/Gore, and Dangerous Content, with hierarchical expansion for adversarial coverage (Zeng et al., 1 Apr 2025).
• Decentralized Systems/Blockchains: IDMs on Ethereum are moderated for hate, threats, profanity, and illicit links, with semantic rules codified for protocol-level enforcement (Xiong et al., 12 Oct 2025).

A typical policy workflow involves delivering human-readable definitions and guidelines alongside model input, enabling dynamic adaptation or expansion of harm categories as regulatory or application requirements evolve (Nandwana et al., 5 Dec 2025).

2. Input vs. Output Moderation: Mechanisms and Tradeoffs

Input-based filtering and output-based moderation are distinct but often complementary strategies:

• Input Filtering: Operates pre-inference, rejecting or rewriting user prompts according to blacklist, pattern-matching, or classifier scores. The classifier $C_{\text{in}}(q)$ blocks unsafe queries upfront (Bakulin et al., 13 Feb 2025).
• Output Moderation: Monitors model responses $r$ with a post-hoc classifier $C_{\text{out}}(r)$, blocking or sanitizing harmful completions (Bakulin et al., 13 Feb 2025). Output-moderation is more robust against adversarial prompt attacks and reduces false positives on benign input.
• Empirical Superiority: FLAME’s engine shows that output moderation reduces jailbreak attack success (ASR) by up to 9×, with average moderation latency of 4.3 ms per message (Bakulin et al., 13 Feb 2025).
• Combined Pipelines: Leading guardrail architectures chain both input and output filters for maximal security. For example, ShieldGemma and Roblox Guard implement scoring modules capable of input or output moderation with tunable thresholds per harm type (Zeng et al., 2024, Nandwana et al., 5 Dec 2025).

	Input Filtering	Output Moderation
Latency	Pre-inference, negligible	Post-inference, ~ms
Attack surface	Susceptible to encoding	Robust to variant prompts
False positive risk	Higher	Lower
Combined use	Standard; input+output	State-of-the-art

3. Algorithms, Models, and Data

State-of-the-art moderation employs transformer-based models, synthetic data augmentation, and, for maximum control, rule-based flows:

• LLM-based Classifiers: Llama Guard (Inan et al., 2023), Roblox Guard (Nandwana et al., 5 Dec 2025), and ShieldGemma (Zeng et al., 2024) use instruction-finetuned LLMs (Llama2-7B, Llama3-8B, Gemma2-9B/27B) for prompt-response classification. Input encoding consists of the prompt, optional model response, and an explicit taxonomy; inference returns binary (safe/violation) labels, violation categories, and CoT rationales in some design variants.
• Rule-based and N-gram Engines: FLAME builds blacklists from adversarial message generation via LLMs, augments with semantic neighbors, and checks generated responses for disallowed n-grams, delivering linear runtime complexity and transparent, updatable policy (Bakulin et al., 13 Feb 2025).
• Multimodal Moderation: ShieldGemma 2 fine-tunes vision-language transformers for binary classification on policy categories, using a policy-conditioning prefix and rationale generation. It employs multi-stage synthetic image generation and “borderline adversarial data generation” for robust coverage (Zeng et al., 1 Apr 2025).
• Human-in-the-loop Moderation: Uncertainty-based moderation dynamically routes unconfident model outputs for human review according to calibration on predictive entropy or margin, achieving near 99% F1 with ~30% moderation load (Andersen et al., 2022).

Model	Core Modality	Data Curation	Output
Llama Guard	Text	High-quality labeled prompts	Category + Safe/Violation
ShieldGemma 2	Image	Synthetic/adversarial image pool	Policy-aligned Yes/No + rationales
FLAME	Text	LLM-generated & semantic variants	N-gram match, transparent
Roblox Guard	Text	Synthetic + RLHF + multi-model	Taxonomy-adaptive labels

4. Adversarial Robustness and Defenses Against Jailbreaking

Attackers actively seek to bypass moderation guardrails via input re-encoding and output obfuscation:

• Jailbreak Prefixes: Role-playing or “DAN-style” prompts can evade input-level filters by lowering classifier harm scores while retaining illicit semantics (Jin et al., 2024). Empirically, attack success rates (ASR) with optimized prefixes reach 70–85% versus 20–30% for naive prompts.
• Cipher Character Techniques: Output guardrails are bypassed by wrapping harmful response tokens in non-ASCII “cipher strings,” pushing harm scores below threshold (Jin et al., 2024). The cipher is optimized via greedy coordinate descent against a shadow classifier mimicking the guardrail API.
• Best-of-N Sampling: Output filtering is particularly important under adversarial sampling, where an attacker selects the first unsafe completion from many. FLAME demonstrates a 9× reduction in ASR under such attacks (Bakulin et al., 13 Feb 2025).
• Complexity-Aware and Multi-Layer Defenses: Advanced frameworks integrate fluency/complexity metrics and secondary LLM-based audit passes, decoding or de-obfuscating outputs before final moderation (Jin et al., 2024).

Defense Technique	Attack Vector Addressed	Example System
Output n-gram block	Sampling/jailbreaking	FLAME (Bakulin et al., 13 Feb 2025)
Cipher complexity	Output obfuscation	JAM (Jin et al., 2024)
Multi-LLM audit	Obfuscated/decoded output	JAMBench (Jin et al., 2024)
Input thresholding	Prompt-based evasion	Llama Guard (Inan et al., 2023)

5. Implementation, Efficiency, and Deployment

Production-grade moderation systems demand both efficiency and flexibility:

• Latency and Throughput: ShieldGemma-2B delivers sub-100ms runtime for text moderation online; vision-LLMs such as ShieldGemma 2 process images in <200ms using quantized inference (Zeng et al., 1 Apr 2025, Zeng et al., 2024). FLAME requires only CPU resources: 4.3 ms/message, 0.1 CPU core, 100 MB RAM per instance.
• Update and Scalability: Policy changes are handled dynamically—by prompt/response injection for adaptive taxonomies (Nandwana et al., 5 Dec 2025), topic/blacklist updates for FLAME (Bakulin et al., 13 Feb 2025), or synthetic data regeneration in ShieldGemma (Zeng et al., 2024). Per-category thresholds permit domain- or application-specific tuning.
• Human-Moderation Load Reduction: Uncertainty-thresholded routing reduces annotation burden by up to 73%, with optimal results at 23–33% human-in-the-loop moderation (Andersen et al., 2022).
• Protocol-level Moderation: On-chain moderation (e.g., Ethereum IDMs) can be implemented either (i) by builder-enforced semantic checks at block assembly (high latency, not scalable), or (ii) by user-provided cryptographic moderation proofs (low-latency, gas-efficient, censorship-resistant) (Xiong et al., 12 Oct 2025).

6. Fairness, Equity, and Multimodal Considerations

Responsible Input/Output moderation balances safety with subgroup parity and the mitigation of unintended bias:

• Fairness Metrics: Frameworks assess diversity (entropy), equal treatment (erasure), stereotype amplification (nPMI between demographic attributes and concepts), and counterfactual parity in filtering rates across subgroups (Hao et al., 2023).
• Operationalizing Equity: Moderation pipelines employ separate thresholds and post-processing constraints to enforce minimal representational gaps, adjust for stereotype amplification, and audit for divergence under attribute swaps.
• Multimodal Moderation: ShieldGemma 2 extends vision-language moderation to synthesized and natural images, noting limitations for images with embedded text (no OCR) or interleaved media in conversation (Zeng et al., 1 Apr 2025). Audio moderation adds signal-processing classifiers (for blank/noise detection), speech-to-text, and metadata/NLP modules (Khullar et al., 2021).

7. Limitations and Future Challenges

Current frameworks exhibit domain-specific and methodological constraints:

• Taxonomy Authoring: Advanced LLM pipelines (e.g., Roblox Guard, Llama Guard) rely on human authors for policy definitions and adaptation at inference time (Nandwana et al., 5 Dec 2025, Inan et al., 2023).
• Severity Scoring and Multi-label: Most present systems provide binary “violation” outcomes, lacking fine-grained or multi-harm analysis (Nandwana et al., 5 Dec 2025, Zeng et al., 2024).
• Adversarial Generalization: Output moderation is vulnerable to sophisticated, dynamic obfuscation that eludes static rule-based checks; complexity-aware and LLM-audited augmentation are active areas of research (Jin et al., 2024).
• Cultural and Contextual Harms: LLMs have persistent difficulty with context-dependent microaggressions or culturally contingent harms (Zeng et al., 2024).
• Deployment: Vision and audio pipelines must address modality-specific issues such as embedded text (OCR), multilinguality, and low-resource dialects (Khullar et al., 2021, Zeng et al., 1 Apr 2025).

Ongoing work focuses on multi-label/severity frameworks, real-time multimodal moderation, policy extensibility, continual learning, symbolic policy integration, and community-driven benchmarking for adversarial robustness (Nandwana et al., 5 Dec 2025, Zeng et al., 1 Apr 2025, Jin et al., 2024).

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Input/Output Moderation now constitutes a comprehensive paradigm, integrating taxonomic modeling, transformer-based classification, semi-automated verification, adversarial robustness, and multi-stage deployment to deliver scalable, adaptable guardrails for interactive AI systems. Continued progress is expected in dynamic taxonomy handling, fairness-aware filtering, and multi-modality, in order to meet both regulatory requirements and emerging adversarial threats across diverse deployment domains.