Prompt injection & security testing
Tested against manipulation attempts, not assumed safe because it works in normal use.
Overview
The risks are specific, and mostly avoidable if you check for them
AI features carry a set of failure modes that don't show up in a typical software security review: prompt injection, where a user manipulates the system into ignoring its instructions, data leakage through a poorly scoped retrieval system, and cost overruns from usage patterns nobody modeled for. None of these are hypothetical, they're common, and mostly avoidable with a proper review.
We audit AI systems, whether we built them or not, against these specific risks: testing for prompt injection, checking what data the system can actually access versus what it should, measuring accuracy against real queries, and reviewing whether the cost model holds up at real usage volume, not just in testing.
What's included
A specific, actionable list, not a generic checklist
Data exposure review
Checking what the system can actually access and return, versus what it's supposed to be scoped to.
Accuracy & quality testing
Measured against real, representative queries rather than a handful of favorable examples.
Cost & rate-limit review
Checked against realistic usage volume, so the cost model doesn't fall apart the first time traffic spikes.
Questions
Common questions about AI security reviews
4 questions
What are the biggest security risks with AI features?
Prompt injection (getting the system to ignore its instructions), unintended data exposure through overly broad retrieval or permissions, and cost or rate-limit issues under real traffic. All three are testable and fixable before launch if someone actually checks for them.
Can you audit an AI system another team built?
Yes, this is a common, standalone request. We don't need to have built the system to review it, though access to its architecture and prompts makes the review faster and more thorough.
How do you test for prompt injection?
By actively attempting the manipulation techniques that show up in the wild, trying to get the system to reveal its instructions, ignore its constraints, or access data it shouldn't, and documenting what succeeds.
Should we do this before or after launch?
Before, if at all possible. It's meaningfully cheaper to fix an issue in review than after it's caused a real incident. That said, a review of an already-live system is still worth doing and often turns up something worth fixing.
Quick question?
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From $799, fixed before we start. NDA signed before we see anything.