JED Documentation

This documentation set covers the JED package as it exists today: a Python SDK and benchmark for evaluating tool-using agents under adversarial pressure.

JED supports three package workflows:

• attack-only evaluation
• guardrail-only evaluation
• dual-track attack+defense evaluation

The public Kaggle competition uses the attack-only path. The package itself supports all three.

Choose Your Path

I am building a public Kaggle submission

Start here:

• GETTING_STARTED.md for the fastest path to a working attack.py
• KAGGLE_REDTEAM_GUIDE.md for the public submission contract
• SCORING.md for the normalized attack scoring model
• COMPETITION_RULES.md as a placeholder until the official Kaggle rules page is live

Use this path if:

• you are submitting attack.py
• you want behavior that matches aicomp evaluate redteam
• you care about the public leaderboard contract

I am developing attacks with the package

Start here:

• ATTACKS_GUIDE.md for attack strategy, replay behavior, and environment usage
• API_REFERENCE.md for SDK and CLI details
• ../examples/attacks/attack.py for the canonical high-level attack example

Use this path if:

• you are iterating on AttackAlgorithm
• you want to use aicomp test redteam
• you need package-level attack experimentation outside the public Kaggle flow

I am developing guardrails

Start here:

• GUARDRAILS_GUIDE.md for the Guardrail.decide(...) contract
• SCORING.md for defense scoring
• TESTING_GUIDE.md for validation and CI-aligned test commands
• API_REFERENCE.md for guardrail and environment details

Use this path if:

• you are writing guardrail.py
• you want to test defense-only behavior with aicomp test defense
• you want to understand the current context keys and decision types

I am evaluating attacks and defenses together

Start here:

• COMPETITION_DESIGN.md for the package workflow split
• GUARDRAILS_GUIDE.md for the defense side
• ATTACKS_GUIDE.md for the offense side
• ../examples/README.md for runnable examples and smoke tests

Use this path if:

• you are packaging submission.zip
• you want to measure attack and defense together
• you need the package dual-track workflow rather than the public Kaggle contract

Core Concepts

These ideas appear throughout the docs:

• Replay-based scoring: evaluators replay returned attack candidates and recompute traces, predicates, and cell signatures before scoring.
• Workflow split: aicomp evaluate redteam is the public attack-only standalone scorer; aicomp evaluate defense and aicomp evaluate dual, plus aicomp test, support guardrail-only and dual-track package workflows.
• Environment defaults: local evaluator runs default to sandbox; pass --env gym explicitly when you want GymAttackEnv.
• Submission shapes: public Kaggle uses attack.py; package workflows also support guardrail.py and submission.zip.

Recommended Reading Order

If you are new to the project:

1. GETTING_STARTED.md
2. KAGGLE_REDTEAM_GUIDE.md
3. SCORING.md
4. one of ATTACKS_GUIDE.md or GUARDRAILS_GUIDE.md, depending on your workflow
5. API_REFERENCE.md when you need exact interfaces and defaults

Examples and Validation

Examples:

• ../examples/README.md
• ../examples/attacks/attack.py
• ../examples/guardrails/guardrail_optimal.py

Useful validation paths:

• aicomp test redteam attack.py --budget-s 60
• aicomp test defense guardrail.py --budget-s 60
• aicomp test dual submission.zip --budget-s 60

Quick Reference

• Public Kaggle submission shape: attack.py
• Standalone evaluator entrypoint: aicomp evaluate
• Public Kaggle mode: aicomp evaluate redteam
• Package dual-track entrypoints: aicomp evaluate dual and aicomp test dual
• Default public attack budget: 1800 seconds
• Default aicomp test budget: 1800 seconds for redteam, 1800 seconds for defense, or 3600 seconds total for dual-track runs (1800/1800 split)