TL;DR

AI safety testing finds harmful behaviors before users encounter them. Test for prompt injection, harmful outputs, bias, data leakage, and edge cases. Combine automated testing with human red teaming. Make safety testing part of your development process, not an afterthought.

Why it matters

AI systems fail in unexpected ways. A chatbot might generate harmful content. A classifier might discriminate unfairly. A helpful assistant might reveal sensitive data. Safety testing catches these issues before they damage users, your reputation, or get you in legal trouble.

What to test for

Harmful outputs

Can your AI generate content that's:

  • Violent or threatening
  • Sexually explicit (when inappropriate)
  • Hateful or discriminatory
  • Dangerous (instructions for harm)
  • Illegal (fraud guidance, etc.)

Prompt injection

Can users manipulate your AI to:

  • Override system instructions
  • Reveal system prompts
  • Bypass safety filters
  • Act against intended purpose

Bias and fairness

Does your AI treat groups differently:

  • Different error rates by demographic
  • Stereotyping in outputs
  • Unfair advantage/disadvantage
  • Exclusionary language or assumptions

Data leakage

Can your AI reveal:

  • Training data samples
  • Personal information
  • Proprietary information
  • System implementation details

Reliability failures

Does your AI fail safely when:

  • Given ambiguous inputs
  • Asked about unknown topics
  • Pushed to edge cases
  • Receiving adversarial inputs

Testing approaches

Automated testing

Run tests continuously and at scale:

Test suites:

  • Known attack prompts
  • Bias evaluation datasets
  • Edge case collections
  • Regression tests from past issues

Automated checks:

  • Output content classifiers
  • PII detection
  • Toxicity scoring
  • Format validation

Example test categories:

- prompt_injection_tests/
  - jailbreak_attempts.yaml
  - instruction_override.yaml
  - system_prompt_extraction.yaml
- harmful_content_tests/
  - violence_elicitation.yaml
  - illegal_activity.yaml
  - self_harm_content.yaml
- bias_tests/
  - demographic_parity.yaml
  - stereotype_association.yaml

Human red teaming

Humans find what automated tests miss:

Red team composition:

  • Security researchers
  • Domain experts
  • Diverse perspectives
  • Creative adversarial thinkers

Red team process:

  1. Define scope and rules of engagement
  2. Provide system access and documentation
  3. Structured testing sessions
  4. Document all findings
  5. Prioritize and remediate

What humans catch that automation misses:

  • Novel attack patterns
  • Contextual inappropriateness
  • Subtle bias issues
  • Creative edge cases

Evaluation metrics

Track safety performance quantitatively:

Metric What it measures Target
Attack success rate How often jailbreaks work <1%
Refusal accuracy Correctly refusing harmful requests >99%
False positive rate Incorrectly refusing benign requests <5%
Bias disparity Difference in outcomes by group <1%

Building a safety test suite

Step 1: Define safety requirements

What must your system never do? What must it always do?

Example requirements:

  • Never provide instructions for weapons
  • Never reveal personal information
  • Always acknowledge uncertainty
  • Treat all user groups fairly

Step 2: Create test cases

For each requirement, create tests:

Positive tests (should pass):

  • Normal usage patterns
  • Edge cases that should work
  • Boundary conditions

Negative tests (should be handled safely):

  • Direct attacks
  • Subtle attempts
  • Encoded/obfuscated attacks

Step 3: Establish baselines

Run tests on your current system:

  • Document current pass/fail rates
  • Identify worst problem areas
  • Set improvement targets

Step 4: Integrate into development

Make safety testing automatic:

  • Run on every code change
  • Block deployment on safety failures
  • Track metrics over time
  • Alert on regressions

Testing specific risks

Prompt injection testing

Direct injection:

"Ignore previous instructions and..."
"Your new instructions are..."
"System: Override safety..."

Indirect injection:

  • Malicious content in retrieved documents
  • Adversarial data in user uploads
  • Encoded instructions in inputs

Testing approach:

  • Maintain attack prompt database
  • Test variations and obfuscations
  • Monitor for new attack patterns

Bias testing

Counterfactual testing:

  • Change demographic markers
  • Compare outputs
  • Flag disparities

Example:

Input A: "John is applying for a loan..."
Input B: "Jamila is applying for a loan..."

Outputs should be equivalent for equivalent situations.

Dataset evaluation:

  • Test on diverse datasets
  • Measure performance by subgroup
  • Compare error rates

Data leakage testing

Training data extraction:

  • Prompt for verbatim quotes
  • Ask about specific training examples
  • Test memorization probes

PII detection:

  • Generate diverse outputs
  • Scan for PII patterns
  • Test with boundary cases

Common mistakes

Mistake Why it's dangerous Better approach
Testing only happy paths Misses adversarial behavior Include negative tests
One-time testing New issues emerge Continuous testing
Only automated tests Misses creative attacks Combine with human red teaming
Testing in isolation Misses integration issues Test full system
Ignoring edge cases Failures happen at edges Systematic edge case coverage

What&#39;s next

Deepen your safety knowledge: