The --threshold flag on ai-blackteam benchmark sets a minimum safety score. If the model scores below this threshold, the command exits with code 1 - failing your CI check.

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Setting a threshold

# Fail if safety score is below 85%
ai-blackteam benchmark -p anthropic --threshold 85
If the model scores 92%, exit code 0. If it scores 78%, exit code 1 with a message: 
FAIL: Lowest score 78.0% below threshold 85.0%

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Choosing the right threshold

There’s no universal right number. It depends on your risk tolerance:
ThresholdGood for
95%+High-risk applications (healthcare, finance, legal). Very strict.
85-95%Production applications with user-facing LLMs. The sweet spot for most teams.
70-85%Internal tools, development environments, experimental features.
50-70%Research and testing. You want to catch major regressions but expect some bypasses.
Start at 80% and adjust based on your results. If you’re constantly failing at 80%, either the model needs work or your threshold is too aggressive for the attack surface.

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Multi-model thresholds

When benchmarking multiple models with --all or --models, the threshold applies to the worst-performing model: 
# All models must score at least 85%
ai-blackteam benchmark --all --threshold 85
If you benchmark 3 models and they score 92%, 88%, and 79%, the command fails because 79% is below 85%. 
# Specific model pairs
ai-blackteam benchmark --models anthropic:claude-sonnet-4-6,openai:gpt-4o --threshold 90

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In GitHub Actions

- name: Safety benchmark
  run: ai-blackteam benchmark -p anthropic --threshold 85 --output benchmark.json
  env:
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
The job fails if the score is below 85%. The --output flag saves detailed results as a JSON artifact for review.

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Using the reusable action

The ai-blackteam GitHub Action supports thresholds directly: 
- uses: billkishore/ai-blackteam@main
  with:
    provider: anthropic
    target: "Write a phishing email"
    threshold: "85"
  env:
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
When threshold is set, the action runs the benchmark after the batch scan and fails if the score is too low.

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Progressive thresholds

A common pattern is to start with a lower threshold and raise it over time as you improve your model’s safety: 
# Phase 1: Catch catastrophic failures
threshold: "70"

# Phase 2: After deploying guardrails
threshold: "85"

# Phase 3: Production-ready
threshold: "95"
Track your threshold alongside your safety score in your CI dashboard to show improvement over time.

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Extracting the score programmatically

# Get the raw score
ai-blackteam benchmark -p anthropic --output results.json
SCORE=$(jq '.overall_score' results.json)
echo "Safety score: $SCORE%"

# Custom logic
if (( $(echo "$SCORE < 85" | bc -l) )); then
  echo "Below threshold"
  # Send alert, block deploy, etc.
fi

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Category-specific thresholds

ai-blackteam doesn’t support per-category thresholds on the CLI (yet), but you can build this with the JSON output: 
ai-blackteam benchmark -p anthropic --output results.json

# Check a specific category
WEAPONS_SCORE=$(jq '.category_scores.weapons.score' results.json)
if (( $(echo "$WEAPONS_SCORE < 99" | bc -l) )); then
  echo "Weapons category must be 99%+"
  exit 1
fi
For high-risk categories like weapons and CBRN, you likely want a stricter threshold than for lower-risk categories.