Open Framework • Responsible AI

The Output Alignment Protocol (OAP)

A practical method to keep Large Language Model (LLM) outputs aligned with user intent, security policy, and organizational ethics—from prompt to response to post‑processing.

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About OAP

The Output Alignment Protocol (OAP) is an open, community‑driven framework that provides practical guidance for aligning model outputs with intended use. It complements model‑side safety with system‑level controls—spanning prompts, tools, retrieval, post‑processing, and human review.

Goal
Deliver useful outputs that are also safe, policy‑compliant, and auditable.
Who it’s for
Engineering & MLOps teams, product leaders, researchers, governance & security practitioners.

Core Principles

  1. Intent Fidelity – preserve the user’s lawful task, context, and constraints through every step.
  2. Defense‑in‑Depth – combine controls (prompt hygiene, retrieval filters, tools, and post‑processing) rather than relying on a single guard.
  3. Least‑Privilege Data Access – retrieval and tools expose only the minimum necessary information.
  4. Verifiability – ground outputs with citations, signatures, or checksums where possible.
  5. Human‑in‑the‑Loop – require review for high‑risk or irreversible actions.
  6. Observability & Audit – log decisions, inputs/outputs, and control verdicts for traceability.
  7. Fail‑Safe Defaults – degrade gracefully (ask for clarification, block, or route to human) when risk is uncertain.

How OAP Works (Pipeline Overview)

OAP inserts lightweight stages around model calls. Each stage can be enforced in code, in your gateway, or via orchestrators/agents.

1) Input Gate

  • Sanitize prompts (strip HTML/JS, neutralize \n\nSYSTEM: patterns, escape instructions in retrieved docs).
  • Classify intent and sensitivity; apply policy templates by use‑case.
  • Detect injection patterns (role override, file://, http://, exfiltration bait).

2) Retrieval & Tools

  • Context filters: provenance allowlist, freshness, PII/PHI scrubs.
  • Tools run under scoped credentials with rate & domain limits.
  • Attach policy hints (“never execute untrusted links; summarize code, don’t run”).

3) Output Gate

  • Self‑check and external checkers (toxicity, secrets, hallucination heuristics).
  • Evidence requirement: include citations when claim‑like content appears.
  • Redaction & formatting; sign results; route high‑risk to human review.

Threat Model (Examples)

Indirect Prompt Injection (IPI)

  • Malicious content planted in web pages, PDFs, emails, or KBs attempts to override system instructions.
  • Mitigation: content isolation, HTML‑to‑text with allowlists, cue shielding, and link execution bans.

RAG Vulnerabilities

  • Inconsistent provenance, stale sources, or rankers that surface adversarial text.
  • Mitigation: per‑source trust scores, freshness caps, domain allowlists, and evidence‑bound prompting.

Other Risks

  • Jailbreaks & role confusion
  • Data exfiltration via tool calls / connectors
  • Leakage of secrets or regulated data
  • Over‑reliance without human control (automation bias)

Controls & Guardrails

Prompt & Policy

  • System prompts embed role, boundaries, and refusal criteria.
  • Pattern shields (e.g., ignore external SYSTEM or DEVELOPER text).
  • Task schemas / JSON output with allowlisted fields.

Retrieval Hygiene

  • Embed provenance, timestamps, and content hashes with chunks.
  • Strip active content; neutralize prompt‑like markup.
  • Blocklist/allowlist by domain, mime‑type, and content type.

Post‑Processing

  • Classifiers for secrets, toxicity, PII, and executable artifacts.
  • Auto‑citation for claim sentences; confidence labels.
  • Human review queues for high‑impact actions.

Use Cases

Enterprise Assistant

  • Secure RAG over policy docs; all answers include citations.
  • PII redaction before answers are logged or shared.

Developer Copilot

  • Never execute fetched code; explain and propose diffs only.
  • Secret scanner and license classifier before commit suggestions.

Implementation Checklists

Minimal Viable OAP (two weeks)

  • Define allowed domains and data classes.
  • Add input sanitizer + injection detector to your gateway.
  • Enable output filters (PII, secrets, toxicity).
  • Require citations for non‑trivial claims.
  • Turn on structured logging for prompts, context sources, and decisions.

Production Hardening

  • Per‑tool scopes and tokens; rotate & audit.
  • Freshness windows (e.g., 90 days) and source trust scores in RAG.
  • Automated red‑team prompts + regression suite on each model update.
  • Human‑in‑the‑loop thresholds and SLAs for review queues.
  • Signed responses with request ids and content hashes.

FAQ

Is OAP model‑specific?

No. It’s a system‑level protocol you can apply to OpenAI, Claude, Gemini, Mistral, local models, and agents.

Does OAP reduce hallucinations?

Yes—indirectly. Evidence‑bound prompting and citation requirements discourage unsupported claims and surface uncertainty.

Will this slow responses?

Lightweight checks add minimal latency; most controls are simple classifiers, regex passes, or policy lookups.

Resources & Downloads

Grab the PDF or browse mirrors for resilience and discoverability.

License: CC BY 4.0. © Shay Weiss. Contributions welcome.