GraphCanon updated Sep 13, 2026 · GitHub synced Sep 13, 2026
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Decision brief
StepShield aids in evaluating temporal guardrail effectiveness on AI agents through step-level annotations, ideal for ensuring security over time.
Good fit when
- When you need to measure the timing of interventions rather than just if they occur
- For benchmarking your AI safeguards against a curated dataset of 9,429 trajectories
Avoid when
- If your project does not require temporal analysis of guardrail performance
- When you seek real-time intervention and do not need pre-defined trajectory datasets
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (7d since push)
- As of Sep 13, 2026
- Provenance
- Not a fork · Personal account
- As of Sep 13, 2026
- Security (OSV)
- 6 low (6 low)
- As of Jul 15, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install stepshieldSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
StepShield provides trajectories and step-level annotations to evaluate the temporal intervention effectiveness required for securing rogue AI agents.
Capability facts
- Languages
- python
Source: github.language+pyproject.toml · Sep 13, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 13, 2026)
pip install -r requirements.txtSource link
Tags
README
1. Install License The code in this repository is licensed under the MIT License. The dataset is licensed under CC BY 4.0. This project was supported by the MOVE Fellowship . For questions or collaboration inquiries, contact .
For agents
This page has a .md twin and JSON over the API.