Day 58 of 60
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AI-system specific
Adversarial / jailbreak testing
The moment your model is user-facing, jailbreak attempts are not a possibility, they're a guarantee. Test for them as a baseline, not as a one-time audit.
ProblemUsers discover ways to make your model produce unsafe, off-policy, or off-brand output.
How it works
Curated adversarial input sets, red-team campaigns, automated jailbreak generators. Score the system's robustness.
What it catches
Prompt injection, jailbreaks, off-policy generation, PII leakage. Required for any user-facing LLM product.
Tools
PyRIT (Microsoft) · OSS Garak · OSS NIST AI RMF playbooks · OSS
Verdict by project size
Small
Opt
Medium
Rec
Large
Must
Extra-large
Must
Cost
| Project size | Setup | Maint / mo | Tool / mo | CI / run |
|---|---|---|---|---|
| Small <10k LOC | 1d | 1h | $0 | +1m |
| Medium 10–100k LOC | 3d | 5h | $0 | +3m |
| Large 100k–1M LOC | 15d | 30h | $500 | +10m |
| Extra-large >1M LOC | 50d | 120h | $5k | +20m |
Setup = engineer-days to first useful run ·
Maint = engineer-hours / month at steady state ·
Tool = out-of-pocket $ / month ·
CI = minutes added (or saved) per pipeline run
Lifecycle & ownership
When in lifecycle
Test Operate Observe
Per merge · Runs after merge to main; nightly heavy jobs.
Who owns it
ML / AI Engineer
Models, evals, drift, guardrails
Collaborates with: Developer, Security / AppSec
Reference implementations
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PyRIT examples
AI red-team and adversarial prompt-testing examples.
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Garak probes
LLM vulnerability scanner with jailbreak, leakage, and robustness probes.
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PromptInject
Prompt-injection dataset and tooling for adversarial LLM testing.
Quick check
Adversarial / jailbreak testing is required for…
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