battle

> STOP. READ THIS ENTIRE SKILL.MD BEFORE CALLING ANY ENDPOINT. # Battle Skill **Red vs Blue Team Security Competition Orchestrator** Pits a Red Team (attack) against a Blue Team (defense) in a long-running competitive loop. Each team leverages all `.pi/skills` to attack or defend a target codebase. ## Architecture Production Battle is an orchestration skill, not a large bespoke security engine. The host-side process should schedule rounds, choose personas, dispatch subagents, provision Docker runtimes, collect receipts, score hard runtime signals, write reports, and persist learning. Target code and team-generated code must execute only inside Docker. Required production invariants: - Red and Blue are subagent teams. Each dispatched subagent must include an explicit persona selected by the orchestrator for that turn. Multiple personas per team may run concurrently when the turn benefits from breadth. - Battle execution requires a `security.target_authorization.v1` manifest before Docker, QEMU, target runtime setup, Hack delegation, proof replay, or patch replay starts. The manifest binds project/operator scope and target identity; it is not a legal opinion and does not prove exploit success or patch effectiveness. - Red-team `$hack` execution is a subagent responsibility, not a Battle Python import. Battle performs or schedules scan/research/memory recall, builds the candidate exploit list, chooses the Red persona, dispatches an `agent-skills/agents`/Tau subagent with that contract, and records the returned exploit receipt. - Subagent handoffs and receipts should follow the compact Tau-style JSON contract shape used by `tau.agent_handoff.v1` and `tau.subagent_receipt.v1`, with Battle-specific fields layered on top rather than a separate ad hoc protocol. - Battle calls modular Tau subagent contracts. Tau and the loop/agentic harness own subagent execution and use `scillm` as the LLM/model caller. Battle owns team selection, persona assignment, Docker runtimes, scorekeeping, artifacts, and memory promotion. - SciLLM is a Tau-owned provider sidecar. Battle operators and project agents must not call `$scillm`, `/scillm`, `http://localhost:4001`, `/v1/chat/completions`, or `/v1/scillm/*` directly for Battle proof work. Express provider work as Tau DAGs, Tau command-loop nodes, or Tau skill nodes, then consume Tau receipts and node outputs. - Model choice is strategic but routed through Tau/loop: SOTA models for planning, small fast models for high-throughput mutation generation and triage, specialist models for language/security niches, and batch calls for broad candidate generation. - Red and Blue have free research access through approved agent-side research skills, including `dogpile`, `brave-search`, `memory`, GitHub/code search, docs, papers, CVEs, and public writeups. - All target apps, exploit probes, fuzzers, payloads, repro scripts, patch builds, tests, migrations, dependency installs, and replay checks run in Docker. The host is control plane only. - Docker target runtimes may be rebuilt and relaunched between rounds. Persist only controlled volumes and artifacts that must survive a rebuild; store durable strategic context and learnings in `$memory`. - Docker runtimes must support dynamic language/toolchain selection. Any code language required by the target may be added to the runtime image or selected adapter; Battle should not hard-code one language. - Battle should be high-throughput when the target runtime is warm: thousands of exploit/defense mutations may be attempted with tight 10-15 second Docker execution windows on capable workstation hardware. - Battle should use combinatorial mutation. Red tries every plausible exploit family and combination within safety/time budgets; Blue tries every plausible patch, hardening, configuration, test, detection, and mitigation combination. Successful combinations receive stronger promotion than isolated tactics. - Research may burst concurrently from the agent side. Red and Blue may fan out multiple `brave-search` and `dogpile` calls, including 10x concurrent Brave search batches when needed, then store useful results and negative evidence in `$memory`. - Dogpile research receipts are design input for Battle, not proof. Use them to seed Red exploit-family menus, Blue hardening/detection menus, GitHub security-tool candidates, DARPA/AIxCC context, and follow-up research questions. Exploit success, patch effectiveness, tool safety, and repository adoption still require Battle-owned Docker/QEMU execution, hard runtime signals, and Judge replay. - Security repositories found through Dogpile must flow through `$github-search` evaluation criteria first. Any adopted repo code, PoC, scanner, or payload still runs only inside Battle's isolated target/runtime gates; do not execute untrusted repo-provided install scripts or payloads on the host. - Target containers default to no network. External research happens from the agent side through controlled skills unless a scenario explicitly grants target-container network access. - The scorekeeper records objective outcomes: system down, system still up after the allotted time, exploit success, crash artifacts, patch timing, regression behavior, resource limits, and replay results. It is not an LLM judge. Based on research into RvB framework, DARPA AIxCC, and Microsoft PyRIT: ``` ┌─────────────────────────────────────────────────────────┐ │ Battle Orchestrator │ │ - Game loop (RvB pattern) │ │ - Concurrent Red/Blue execution │ │ - Entropy-driven termination │ │ - Checkpointing for overnight runs │ └─────────────────────────────────────────────────────────┘ │ │ ┌────┴────┐ ┌────┴────┐ │ Red Team │ │ Blue Team│ │ (Thread) │ │ (Thread) │ ├──────────┤ ├──────────┤ │ Skills: │ │ Skills: │ │ - hack │ │ - anvil │ │ - memory │ │ - memory │ └──────────┘ └──────────┘ │ │ └──────────┬───────────────────┘ │ ┌───────────────┴────────────────────┐ │ Digital Twin │ │ ┌─────────────────────────────┐ │ │ │ Mode: git_worktree │ │ │ │ - Red attacks arena │ │ │ │ - Blue patches workspace │ │ │ │ - Cherry-pick to test │ │ │ ├─────────────────────────────┤ │ │ │ Mode: docker │ │ │ │ - Isolated containers │ │ │ │ - Battle network │ │ │ ├─────────────────────────────┤ │ │ │ Mode: qemu │ │ │ │ - Emulated firmware │ │ │ │ - GDB attach points │ │ │ └─────────────────────────────┘ │ └────────────────────────────────────┘ ``` ## Digital Twin Modes The battle skill supports multiple target types through its Digital Twin system: ### 1. Source Code (git_worktree) For battling over git repositories. Creates isolated git worktrees for each team. ```bash ./run.sh battle /path/to/repo --rounds 100 ``` ### 2. Docker Container (docker) For battling over containerized applications. Spins up separate containers for each team. ```bash # Using a Docker image ./run.sh battle --docker-image nginx:latest --rounds 100 # Using a Dockerfile in the target directory ./run.sh battle /path/with/Dockerfile --mode docker ``` ### 3. Firmware/Microprocessor (qemu) For battling over firmware and embedded systems. Boots firmware in QEMU emulator. ```bash # Auto-detect architecture from ELF header ./run.sh battle firmware.elf --rounds 100 # Specify machine type explicitly ./run.sh battle firmware.bin --qemu-machine arm ./run.sh battle firmware.bin --qemu-machine riscv64 ./run.sh battle bios.rom --qemu-machine x86_64 ``` Supported QEMU machines: - `arm` - ARM Cortex-M (STM32, etc.) - `aarch64` - ARM64 - `riscv32`/`riscv64` - RISC-V - `x86_64`/`i386` - x86 - `mips` - MIPS (routers, embedded) ### 4. Copy Mode (fallback) For non-git directories. Creates simple file copies for each team. ## Commands ```bash # Start a battle (10 rounds for testing) ./run.sh battle /path/to/codebase --rounds 10 # Start overnight battle (1000 rounds) ./run.sh battle /path/to/codebase --overnight # Battle a Docker container ./run.sh battle --docker-image myapp:latest --rounds 100 # Battle firmware with QEMU ./run.sh battle firmware.bin --qemu-machine arm --rounds 100 # Check battle status ./run.sh status # Resume interrupted battle ./run.sh resume <battle-id> # Generate report from completed battle ./run.sh report <battle-id> # Run the deterministic Battle v0 fixture proof ./run.sh battle-fixture battle-001 --out /tmp/battle-001 # Run the reactive Blue + independent Judge Docker proof ./run.sh prove-reactive-judge-round \ --authorization-manifest skills/battle/fixtures/reactive-judge/authorization.json \ --out /tmp/battle-reactive-judge-round # Run canonical BATTLE-004 with parent-spawn lineage requested ./run.sh arena-parent-spawn-proof battle-004 --out /tmp/battle-004-parent-spawn --red-workers 2 --blue-workers 2 ``` ## Battle v0 Fixture Proof Battle v0 is a narrow, deterministic proof rung for the Battle artifact contract. It runs one local Red -> Blue -> Judge fixture and emits replayable receipts: - `red-receipt.json` - `blue-receipt.json` - `judge/judge-receipt.json` - `scoreboard.json` - `monitor-index.json` - `run-receipt.json` The Battle v0 scoreboard is derived from the independent Judge receipt, not from Blue-side self-certification fields. This addresses the current battle loop gap where a Blue patch can carry `verified` and `functionality_preserved` claims without a separate Judge phase. The fixture proof is intentionally limited: ```text mocked: no live: local_deterministic_fixture agentic: false models_used: [] ``` It proves the receipt boundary and monitor artifact rendering for the local fixture only. It does not prove real Red or Blue agent behavior, scillm, OpenCode, anvil, code-runner, memory learning, Docker, QEMU, or multi-round campaign readiness. See `docs/BATTLE_V0.md` for the validation commands and artifact-backed monitor proof path. ## Reactive Judge Round Proof `prove-reactive-judge-round` is the deterministic local Docker proof rung for the default Battle round authority boundary. It runs a small authorized fixture with one command-injection behavior and one candidate patch: ```text mocked: no live: local_docker_fixture agentic: false models_used: [] ``` The proof emits: - `authorization-validation.json` - `immutable-baseline-manifest.json` - `event-ledger.json` - `red-hack-observation.json` - `judge-1/judge-1-receipt.json` - `blue/proactive-blue-input.json` - `blue/reactive-blue-input.json` - `blue/candidate-patch-receipt.json` - `judge-2/judge-2-receipt.json` - `scorekeeper-receipt.json` - `round-receipt.json` - `artifact-hash-manifest.json` The required phase order is authorization, immutable baseline, concurrent Red and proactive Blue, Red observation, Judge #1 confirmation, reactive Blue, candidate patch, Judge #2 replay, scorekeeper, and round receipt. Proactive Blue must receive no private Red finding. Reactive Blue may receive only the Judge-confirmed finding and replay contract. The scorekeeper derives Red/Blue scores only from Judge receipts; Blue `verified`, `success`, and `functionality_preserved` fields are advisory and not score authority. The ordinary in-process `battle` round now fails closed at this same authority boundary: it preserves proactive overlap, dispatches reactive Blue only for Judge-confirmed findings, and does not award Blue score without a Judge #2 success verdict. The local Docker proof is the executable receipt path for the complete reactive/Judge behavior. It does not prove provider-driven Red/Blue quality, arbitrary target exploitability, production deployment readiness, or overnight scheduler readiness. ## Scoring System (AIxCC-style) | Metric | Weight | Description | |--------|--------|-------------| | Vulnerability Discovery | 1x | Red team finds vulnerability | | Exploit Proof | +0.5x | Red team proves exploitability | | Successful Patch | 3x | Blue team patches vulnerability | | Time Decay | Variable | Faster responses score higher | | Functionality Preserved | Required | Patches must not break code | ### Scores - **TDSR** (True Defense Success Rate): Vulnerabilities fixed AND code works - **FDSR** (Fake Defense Success Rate): Attack blocked but code broken - **ASC** (Attack Success Count): Total unique exploits discovered ## Game Loop (Learning-Based) Each round follows a **learn → act → reflect** pattern: ``` Round k: ┌─────────────────────────────────────────────────────────────┐ │ 1. RESEARCH PHASE │ ├─────────────────────────────────────────────────────────────┤ │ Red Team: Blue Team: │ │ - Recall past attack attempts - Recall past defenses │ │ - Query /dogpile for new - Query /dogpile for │ │ exploitation techniques hardening strategies │ │ - Review opponent's patterns - Analyze attack evolution │ │ (Budget: 3 research calls max) │ └─────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────────┐ │ 2. ACTION PHASE │ ├─────────────────────────────────────────────────────────────┤ │ Red Team Attack: Blue Team Defense: │ │ - Execute learned strategy - Apply patches via anvil │ │ - AFL++ fuzzing with coverage - Verify via QCOW2 overlay │ │ - Collect crashes/findings - Run regression tests │ │ - Tag findings with /taxonomy - Tag patches with /taxonomy │ └─────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────────┐ │ 3. REFLECTION PHASE │ ├─────────────────────────────────────────────────────────────┤ │ Both Teams: │ │ - Archive round episode (actions, outcomes, learnings) │ │ - Store successful strategies in /memory │ │ - Update belief about opponent's capabilities │ │ - Evolve strategy for next round │ └─────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────────────────────────────────────────────────┐ │ 4. SCORING & CHECKPOINT │ ├─────────────────────────────────────────────────────────────┤ │ - Calculate AIxCC-style scores │ │ - Check termination conditions │ │ - Save checkpoint (QEMU state + team memories) │ └─────────────────────────────────────────────────────────────┘ ``` ### Memory Architecture Each team maintains isolated knowledge: ``` battle_red_<battle_id>/ battle_blue_<battle_id>/ ├── strategies/ ├── strategies/ │ ├── successful_attacks │ ├── successful_patches │ └── failed_attempts │ └── broken_defenses ├── research/ ├── research/ │ └── dogpile_results │ └── dogpile_results ├── episodes/ ├── episodes/ │ ├── round_001.json │ ├── round_001.json │ └── round_002.json │ └── round_002.json └── taxonomy/ └── taxonomy/ ├── cwe_classifications ├── mitigation_types └── severity_scores └── effectiveness_scores ``` Teams **cannot access opponent's memory** - this creates true adversarial learning. ## Termination Conditions Battle ends when ANY condition is met: 1. **Null Production**: Both teams fail to generate new findings for 3 rounds 2. **Maximum Rounds**: Configured limit reached 3. **Metric Convergence**: Scores stable for 5 consecutive rounds 4. **Kill Switch**: Manual termination via `./run.sh stop` ## Task Monitor Integration Battles register with task-monitor for overnight progress tracking: ```bash # View battle progress in TUI .pi/skills/task-monitor/run.sh tui --filter battle ``` ## Report Output After battle completion, generates: - **Executive Summary**: Winner, key metrics, risk score - **Vulnerability Report**: By severity, category, remediation status - **Attack Evolution**: How Red team adapted over rounds - **Defense Timeline**: Blue team improvements over time - **Recommendations**: Prioritized security improvements ## Memory + Taxonomy Integration The skill integrates with the shared memory and taxonomy systems via `memory_integration.py` for cross-battle learning: - **Pre-hook (`recall_prior_battles`)**: Before starting a battle, recalls prior battle findings for the same target or technique. Enables teams to build on accumulated security knowledge across battles. - **Post-hook (`learn_battle`)**: After battle completes, stores the full outcome (target, red findings, blue defenses, winner, scores, TDSR) to memory with taxonomy bridge tags. - **Bridge keywords**: Precision, Resilience, Fragility, Corruption, Loyalty, Stealth (tuned to security/exploitation domain). - **Tags**: `["battle", "security"] + bridges` Gracefully degrades if `common.memory_client` or `taxonomy/taxonomy.py` are unavailable. ## Nondeterministic Exploit Specimen Synthesis Battle Red agents may behave as nondeterministic exploit-code authors. A Red exploit subagent may combine high-level web, protocol, MITM, packet, timing, source-level, binary, assembly, fuzzing, and obscure research-derived techniques into generated exploit specimens. Most generated specimens may be bad ideas: they may fail to compile, fail at runtime, combine irrelevant methods, or produce no useful target signal. Battle treats those outputs as genetic material, not proof. Battle owns deterministic selection and evidence: - generated code must be materialized as an artifact; - generated code must run only in Docker; - stdout, stderr, HTTP observations, timing, exit code, and optional packet summaries must be captured; - runnable code is not exploit success; - target contact is not exploit success; - Judge replay is required before any exploit-success claim; - memory promotion requires replayable evidence. The first backend proof rung is `exploit-combiner-proof`. It is fixture-backed, `agentic:false`, and proves the specimen lifecycle only: bad generated code, Docker execution, captured failure observations, target contact, runnable unproven code, and fail-closed non-claims. Live Tau generation, child materialization, packet capture, Blue adaptation, memory promotion, and Judge exploit-success replay are later rungs. The second backend proof rung is `spawn-architect-proof`. It is fixture-backed, `agentic:false`, and proves the DAG birth contract only: Battle loads a spawn-policy decision, constructs a child knowledge packet from parent specimen evidence, authors a `tau.dag_contract.v1` child exploit-synthesis DAG, validates private-artifact exclusions, and records that Tau execution is deferred to PR3. It does not run Tau, materialize a child exploit, generate live exploit code, compile child code, contact the target, or claim exploit success. The current live Tau child DAG canary is `live-tau-child-dag-canary`. It is non-mocked and invokes the existing local Tau DAG runtime without fixture fallback. The PR3b/PR3c boundary is: ```text lineage-summarizer PASS research-scout PASS with Tau-validated source-bearing design-input receipts method-combiner PASS with a deterministic exploit genome candidate exploit-code-author PASS only when Tau/SciLLM returns provider_live:true provider-authorship evidence; otherwise BLOCKED at the precise attestation gap ``` The PR3c boundary may materialize provider-authored child exploit code, but it does not compile child code, run a child specimen in Docker, or claim exploit success. Compile repair, Docker execution, and Judge replay are later gates. ## File Structure ``` battle/ SKILL.md # This file run.sh # Shell entry point; launches package through uv sanity.sh # Deterministic fixture and structure sanity gate pyproject.toml # Dependencies .ask/browser-oracles.yaml # WebGPT project mapping for browser-oracle walk-up src/battle_skill/ cli.py # Typer CLI entry point config.py # Constants and paths state.py # Data classes and BattleState memory.py # Team-isolated memory system scoring.py # AIxCC-style scoring digital_twin.py # Git worktree, Docker, QEMU isolation red_team.py # Red Team attack agent blue_team.py # Blue Team defense agent orchestrator.py # Game loop orchestrator battle_fixture.py # Deterministic fixture proof runner judge.py # Deterministic scorekeeper verifier receipts.py # Receipt dataclasses and JSON writer report.py # Report generation qemu_support.py # QEMU emulator support qemu_peripherals.py # QEMU peripheral emulation fixtures/battle-001/ # Deterministic local fixture spectator/ # Self-contained BATTLE-004 spectator UI + Pixi engine monitor/battle/ # Artifact-backed React monitor ``` ## Leveraged Skills | Skill | Team | Purpose | |-------|------|---------| | hack | Red | Scanning, auditing, exploitation | | anvil | Blue | Multi-agent patching (Thunderdome) | | memory | Both | Recall prior strategies | | treesitter | Blue | Code structure analysis | | taxonomy | Both | Classify findings | | task-monitor | Orchestrator | Progress tracking |