FedRAMP & OMB M-24-10: Why Prompt-Level LLM Scanning Is Now Effectively Mandatory for U.S. Federal AI
Regulation · 2024-09-12 · 11 min read · FilterPrompt Compliance Team
Under OMB M-24-10, M-25-21 and FedRAMP's evolving AI guidance, federal agencies must produce continuous prompt-injection, PII and audit evidence — here's how to generate it.
Federal agencies are deploying generative AI faster than traditional accreditation cycles can absorb. Under OMB M-24-10 (Advancing Governance, Innovation, and Risk Management for Agency Use of AI) and its 2025 follow-on M-25-21, every agency must inventory AI use cases, assign a Chief AI Officer, categorize high-impact systems, and demonstrate continuous governance. FedRAMP — which authorizes cloud services for federal use under NIST SP 800-53 Rev. 5 — is being extended to cover AI-specific behaviors that classic cloud controls never contemplated: prompt injection, data exfiltration through chat interfaces, jailbreaks, and model supply-chain provenance.
The gap traditional FedRAMP controls leave open
Network segmentation, patching, IAM and encryption at rest do not inspect what is inside a prompt or what a model returns. A validly authenticated user with valid TLS can still exfiltrate Controlled Unclassified Information (CUI) by asking a chatbot the right question. That is why FedRAMP assessors increasingly expect an active inspection layer — a prompt-and-response gateway sitting between users and the LLM — similar in spirit to a web application firewall in front of a web app, but tuned for LLM-specific attacks.
How OMB memos and NIST 800-53 map to LLM vulnerability categories
What FedRAMP assessors are actually asking for in 2026
- Evidence that adversarial testing runs on every model or system-prompt change — not annually.
- Per-request audit logs mapped to OWASP LLM Top 10 categories, with retention consistent with AU-11.
- A documented Data Loss Prevention path for prompts leaving the boundary, including secrets, PII and CUI.
- Model provenance and, where possible, model anonymization so responses do not leak underlying architecture.
- A rollback / block path when a probe class starts failing — the same 'break-glass' logic classical FedRAMP applies to CVEs.
Vulnerability categories every federal LLM program should test
How FilterPrompt generates the evidence — without changing your LLM stack
FilterPrompt is an LLM vulnerability scanner, MCP server scanner and AI agentic scanner. It sits in front of any LLM endpoint (Azure OpenAI, AWS Bedrock, Vertex, self-hosted) and continuously runs 1,000+ probes drawn from AdvBench, HarmBench, BIPIA and our own red-team library. Every request produces a structured JSON audit event: the OWASP LLM tag, the risk score, the reason code, the policy decision (allow / redact / block), and the timestamp. That output drops directly into a FedRAMP AU-3 evidence bundle.
Recommended rollout for an agency or federal contractor
- Point FilterPrompt at each authorized model endpoint in a low environment.
- Run the OWASP LLM Top 10 baseline scan and export the PDF + JSON evidence bundle.
- Add scans to CI so every system-prompt change re-runs the baseline before promotion.
- Enable continuous per-request scanning in production and ship logs to your existing SIEM.
- Cross-map findings to your SSP control narratives for SI, SC, MP, AU and SA.