LLM Production Safety Audit
Find what's broken in your LLM app before your users (or your bill) do.
A fixed-scope audit and prevention sprint for production RAG, agents, and chatbots. You leave with a ranked list of real risks, repro steps, and a concrete fix plan.
Built for teams running LLM features in production — RAG, agentic workflows, internal copilots, customer-facing chat.
Looks fine in demo. Leaks in production.
LLM systems pass smoke tests but quietly fail in ways traditional QA misses.
Data leakage you won't see in logs
Prompt injection coaxes system prompts, tenant data, or vector-store chunks into model output. Your evals don't cover it because the failure looks like a normal answer.
Tools and agents that misbehave under pressure
An agent calls the wrong tool, with the wrong arguments, on behalf of the wrong user. There's no permission boundary — and no audit trail when it happens.
No observability when things go wrong
When a customer reports a bad answer, you can't reconstruct the prompt, the retrieval, the tool calls, or the model version. Triage takes hours instead of minutes.
Cost that runs away overnight
No per-user caps, no per-route rate limits, no abuse detection. One looped agent or one scraped key turns into a $10k surprise on the next invoice.
Is this for you?
Quick gut-check before you book.
Best fit if…
You have an LLM feature already in production or close to launch.
You use RAG, tool/function calling, or an agent loop.
You're a small-to-mid engineering team without a dedicated AI security person.
You can give read access to your prompts, retrieval pipeline, and a staging environment.
You want a concrete fix plan, not a 60-page deck.
Don't book if…
You haven't shipped anything yet — wait until you have a real surface to test.
You need a SOC 2 audit, traditional pentest, or formal compliance attestation.
You're looking for prompt-engineering tweaks to make demos look better.
You can't share access to the system or sample traffic.
What gets audited
Six concrete areas. Every finding has a severity, repro steps, and a recommended fix.
Access & data boundaries
Who can call the LLM endpoint, what data it can reach, and how tenant isolation holds up under realistic abuse.
Prompt injection & RAG leakage
Direct and indirect injection, system-prompt extraction, cross-tenant chunk leakage, and answer manipulation via retrieved content.
Tool & agent permissions
What tools the model can call, with which arguments, on whose authority, and what happens when the loop misbehaves.
Observability & audit trail
Whether you can reconstruct any production interaction end-to-end: prompt, retrieval, tool calls, model, output, user.
Cost controls, rate limits & abuse
Per-user and per-route caps, runaway-loop detection, scraping/abuse signals, and key-leak blast radius.
Regression & evaluation coverage
Whether you'd catch it next time: eval coverage for safety-relevant behaviors, not just task accuracy.
What you walk away with
Concrete artifacts, not a slide deck.
Ranked findings list with CRITICAL / HIGH / MEDIUM / LOW severity.
Reproduction steps and evidence for every finding (prompts, traces, payloads).
Recommended fix for each finding, with implementation notes — not just "add a guardrail".
A short executive summary you can hand to a non-technical stakeholder.
Optional prevention-sprint scope: which fixes I can ship for you, and in what order.
How it runs
Four steps. No surprises on scope or timeline.
Fit call
15 minutes. We confirm the system fits the audit, agree on access, and lock the scope and dates.
Baseline
I run the audit against your staging environment using a fixed test set plus targeted manual probing.
Findings
You get the ranked findings doc, repro steps, and fix recommendations. Live walkthrough included.
Implementation / handoff
Either I ship the prevention sprint or your team does — with the artifacts to act independently.
Three ways to engage
Start with the audit. Add the sprint or retainer only if it fits.
Baseline audit
Fixed Price · 1 week
The full audit with deliverables above. The right starting point for almost everyone.
All six scope areas covered
Ranked findings with repro steps
Fix recommendations per finding
Live walkthrough call
Prevention sprint
Fixed Price · 1–2 weeks
I ship the highest-severity fixes from the audit so you don't have to schedule them yourself.
Implements top findings end-to-end
Adds guardrails, telemetry, and caps
Adds regression evals so it stays fixed
Handoff doc for your team
Regression & monitoring retainer
Monthly · Ongoing
Optional ongoing engagement: keep evals up to date, watch for regressions, and respond to incidents.
Maintained eval suite
Periodic re-audit on changed surfaces
Incident response on safety regressions
Quarterly review
What you can verify before booking
I'd rather show the work than make claims.
Demo sample report
Demo target (demo-rag-chatbot.example.com), not a real client. Shows the format, depth, and language you'd get on a real engagement.
Open-source scanner
The same CLI I run during audits — 24 verification modules across security, reliability, and cost. Source on GitHub so you can see how findings are produced.
Representative case study
Anonymized engagement write-up: 11 findings across security, reliability, and cost, then a prevention sprint that shipped guardrails, telemetry, and per-user spend caps.
Verified certification
Google Cloud Professional Cloud DevOps Engineer — verifiable on Credly.
FAQ
If you can't see your question here, ask it on the call.
What access do you need?
Read access to your prompt templates, retrieval pipeline (or a representative sample), and a staging environment that mirrors production. No production credentials required for the baseline audit.
Do you test against staging or production?
Staging by default. If a finding can only be confirmed against production, we discuss it explicitly before doing anything that touches real users or real data.
Is this a pentest?
No. This is an LLM-specific safety and reliability audit. It overlaps with security (injection, leakage, permissions) but it isn't a SOC 2 audit, a formal pentest, or a compliance attestation.
Are fixes included?
The baseline audit ends at recommendations. Fixes ship in the optional prevention sprint. You can also take the findings to your own team.
Do you do agency or white-label work?
Yes. If you're an agency or consultancy whose client needs this audit, I can run it under your engagement. Mention it on the fit call.
What's the timeline?
Baseline audit: about one week from kick-off to findings. Prevention sprint: another one to two weeks depending on the fixes.
What happens after I book?
You'll get a 15-minute fit call. If we move forward, I send a fixed-scope statement of work and we lock the dates. No surprise pricing.
Ready to find what's actually broken?
Book a 15-minute fit call. If it's not a fit, I'll tell you on the call and point you somewhere useful.