Install payload
Install payload is sparse; verify before rollout decisions.
33% (1/3)
1 trusted · 2 review in this set — compare to see which signals differ.
3 results in this view
3 trust signals differ in this sample: Package trust, Source provenance, Submitter
Signals differ on Package trust, Source provenance, Submitter — add entries to compare before you install.
Rollout signal scan
Biggest gaps: metadata review, package integrity. 1 entries have 2+ required gaps.
Install payload
Install payload is sparse; verify before rollout decisions.
33% (1/3)
Adoption queue
2/3 visible results are in hold tier and need mitigation before adoption.
1 blockers: Metadata review
70/100
Request metadata review from maintainers or internal owners.
skills/prompt-injection-defense-guardrails · trust trusted · confidence 83%
2 blockers: Metadata review, Install payload
36/100
Request metadata review from maintainers or internal owners.
Add install/config payload for reproducible team rollout.
Collect package checksum or signed artifact information.
tools/pyrit · trust review · confidence 50%
3 blockers: Metadata review, Safety notes
22/100
Request metadata review from maintainers or internal owners.
Capture safety notes with misuse/guardrail guidance.
Add install/config payload for reproducible team rollout.
tools/promptfoo · trust review · confidence 33%
Decision confidence
2/3 results are low-confidence and need review before adoption.
Confident candidate for staged adoption.
74/100
skills/prompt-injection-defense-guardrails · trust trusted
Hold adoption until Metadata review, Package integrity are resolved.
36/100
tools/pyrit · trust review
Hold adoption until Metadata review, Safety notes are resolved.
22/100
tools/promptfoo · trust review
Freshness distribution
Median age 92 days; 1 fresh of 3 scanned. Re-verify the oldest entries.
Oldest entries in this view
Theme distribution
67% of this view shares the top theme. Leading themes: security, ai-red-teaming, ai-safety.
9 distinct themes across 3 scanned
Open-source prompt testing and red-teaming framework for LLM outputs, regressions, evaluations, and security checks.
Build layered defenses against prompt injection, data exfiltration, and unsafe tool execution in AI agent systems.
Open-source Python framework from Microsoft for identifying generative AI safety and security risks through automated and human-led red-team assessments.