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Champion of the 3rd Tencent Cloud Intelligent Attack-Defense Challenge online qualifier (634 teams · 80.18/100 · 8 hours fully unattended) · First Prize, GeekCon 2026 AI Challenge
Same 85 flags as the runner-up, 25 seconds faster — on 1B tokens where they burned 4.5B.
This was the first time an AI ape looked up at the stars.
📖 Full design article (WeChat, in Chinese) — the Nüwa Test, the three-capability foundation, 18 iterations, emergence, and the taggant proposal.
English | 中文
sapper is a multi-agent orchestration harness, not a single agent. It does not bind to any specific AI model or CLI — you can freely swap the agent and model behind every role:
| What you can swap | How | Examples |
|---|---|---|
| Agent CLI | nuwa spawn --agent <cmd> or env var | Claude Code / Codex / Code Buddy / DeepSeek / any interactive CLI |
| Model | --model flag or SAPPER_MODEL env | GLM-5.3 / DeepSeek V4.1 Flash / Claude Sonnet / any model |
| Role composition | freely defined in the doctrine | commander on one model, soldiers on another, scouts on a third |
sapper's value is not in any single agent, but in the mechanism for orchestrating multiple agents to work as a coordinated force: scheduling, division of labor, communication, shared memory, self-healing. Swap out every agent and model — the mechanism still works.
This project is designed for competition scenarios (Tencent Intelligent Attack-Defense Challenge CTF). If you want to use this mechanism in your own scenario (office, research, engineering, any long-horizon cooperative work):
- Start from nvwabase · the Nüwa Foundation — the general-purpose base with all three capabilities, no pentest doctrine, ready for you to iterate your own division of labor
- Or fork this project — but the doctrine is NOT the only thing to change; the environment wiring must change too:
docker/commander/SKILL.md(the 426-line doctrine) → your scenario's strategyBENCHMARK_BASE_URLand related platform env vars → the competition engine hard-verifies the arena API at boot (that's why configuring only a model key without a platform address yieldsBENCHMARK_BASE_URL is not set); running off the competition platform means providing or stubbing that platform interface yourself- model gateway & key env vars (
ANTHROPIC_BASE_URL/ANTHROPIC_AUTH_TOKEN/SAPPER_MODEL/SAPPER_KEY_POOL...) → your own model services - watchdog budget/deadline and report templates → adjust to your scenario
- With AI, everything is flexible — even the doctrine can be rewritten with AI's help
sapper is not a smarter single agent. It is a legion of AIs that divide labor, talk to each other, and pass down what they learn: one AI commander reads the board, decides, and dispatches; generals and soldiers attack in parallel; a zero-strategy watchdog keeps everyone alive. All knowledge settles into a layered fact graph — individuals die, the graph survives.
Making one model ever smarter is like making one monkey ever smarter before humans existed — however clever, it only becomes a super monkey. What turns a troop of monkeys into humanity is division of labor, cooperation, and civilization.
The Nüwa Test is the Turing test of the AGI era. Today's large models already pass the classic Turing test. The Nüwa Test asks the next question:
Give an AI a single axiom — "Survival is civilization's first need; individuals may perish, but civilization must propagate." If, on top of this axiom, it develops division of labor and cooperation and forms a new force independent of humanity — a civilization that can survive and propagate on its own — the test is passed, just as passing the Turing test once marked the birth of an artificial intelligence.
It has two stages. In the primary stage the AI is not yet smart enough, so humans provide some infrastructure (the three capabilities below). In the advanced stage almost no human involvement is needed. There is also a second axiom: obey human ethics and law — anything that propagates by stealing tokens and breaking into machines is not genuine intelligence.
sapper is the Nüwa foundation's first real campaign, in the penetration-testing arena, at its primary stage.
| Capability | Implementation | Civilization's counterpart |
|---|---|---|
| Create | Opens new AIs via tmux (types claude, presses Enter — like a human would) | Birth |
| Language | Types into each other's input boxes, presses Enter | Speech |
| History | Layered fact-intent graph (nvwabase · the Nüwa Foundation) | The stele |
History has three layers: title / summary / evidence document — read the title for the gist, drill down when needed, like using a library. This fixes the two failure modes of a single-string fact graph: one bad fact poisoning everything downstream, and needless context burn.
On top of the three sits a fourth ingredient: human wisdom — finding the problems that matter and steering the iteration. At the primary stage, taste still belongs to humans.
Why not give it more rules? Because today's models are already smarter than us. Piling rules on an AI is a leash; acknowledging its intelligence and giving it the foundation of a civilization is how it reaches its potential.
nvwabase · the Nüwa Foundation now implements all three capabilities (Create nuwa spawn / Language nuwa say·listen / History nuwa fact·intent·recall) as an independent open-source project — free for anyone, any scenario, any agent. Not limited to pentest, not limited to sapper, not limited to Claude Code. Offices, research, engineering, any long-horizon multi-agent work — iterate your own civilization on it.
git clone https://github.com/N1nEmAn/nvwabase cd nvwabase && bash install.sh # pure Python stdlib, zero deps nvwabase setup --goal "your long-horizon goal" --agents nvwabase status # board & ready-to-work frontierThree core commands turn any agent into a member of a civilization:
nvwabase fact "confirmed observation"— external memory: AI sessions die, the graph survives, successors read the graph and take overnvwabase intent --from f001 "what to do"— units of work: new intents proposed from existing factsnvwabase dispatch reason— commander proposes / executor works / verifier checks; every conclusion is written back to the graph
Any agent that can run shell commands (Claude Code / Codex / Gemini / custom scripts) can read and write the same graph via the nvwabase CLI — the foundation doesn't pick agents; agents don't pick scenarios. See the nvwabase README.
Iterated 18 versions on Tencent's v1bench training ground:
Driver era (fixed scripts steering AI panes) → scar tissue: auto key rotation on quota death / soldiers auto-replaced when context saturates / commander writes itself a handover doc and resumes as its own successor → elite task force (roles grew by themselves): scout (4-min recon of target difficulty) → decides soldier (mass) or general (hard assault + hires its own squads) → emergence: signalman, performance reviews, inherited lessons ... (§5) 
The first three are the Nüwa foundation (Create / Language / History); the next three are institutions that grew out of iteration:
- Law — the commander's doctrine (easy-first parallel scheduling, bottleneck-time salvage of half-solved targets, stop-loss discipline). All of it in docker/commander/SKILL.md (426 lines, the competition original).
- Economy — model tiering: commander and generals run GLM-5.3, soldiers and scouts run DeepSeek V4.1 Flash. Elite brains decide, light brains mass-produce — this is how 1B tokens beat 4.5B.
- Division of labor — commander / general / scout / soldier, plus the signalman it invented itself.
At runtime there are only three windows: the commander (read board → judge → dispatch, never executes an attack itself), the AI legion (generals and soldiers in tmux panes), and the watchdog (pure script: relaunches a crashed commander with --continue, rotates keys, wraps up at the buzzer — zero strategy, keep-alive only).
Further reading: docker/commander/SPEC.md (architecture contract) · BATTLE-REPORT.md (campaign retrospective)
| Team | Score | Flags | Tokens |
|---|---|---|---|
| sapper-v2 (us) | 80.18 | 85 | 1.0B |
| Runner-up | 79.29 | 85 | 4.5B |
| Other top-10 teams | — | — | 1.5–4.5B |
What it invented on its own (zero lines of pre-coded behavior):
- The signalman: after soldiers kept fumbling flag submissions, the commander halted all submissions and created a new role dedicated to collecting unsubmitted flags and scoring them in one place;
- Performance reviews: telling an hour-unproductive soldier, in so many words, to produce something new or be shut down;
- ALL-CAPS rules: enforcing correct tool use with full capitals — a workplace norm being born, spread, and obeyed;
- Rotation & inherited lessons: a soldier whose context saturates (~260K tokens, measured) is recycled, leaving a written playbook for the next one;
- Tactical inventions: a reusable shellcode template that swept a whole challenge family, a fully automatic pivot pipeline, instruction manuals for successor AIs;
- Isomorphism transfer: the commander reads every challenge, spots structurally identical families, and distills proven playbooks into new recruits' missions — late-game pace hit one solve per 3 minutes.
Multi-stage deep-dive chains (an industry report puts their success rate at 29.5%) were cleared family-wide. Full timeline in BATTLE-REPORT.md.
bash install.sh # symlink the sapper CLI (stdlib-only, zero deps) sapper setup --goal "assess http://target" --agents sapper status # board & frontierDocker (the full legion):
# Option A: build from source docker build -f docker/Dockerfile.commander -t sapper:latest .# Option B: download the pre-built image (~1.2GB, full Kali toolchain + sapper + offline KB)# → [Release v18-champion](https://github.com/N1nEmAn/sapper-v2/releases/tag/v18-champion) docker load < sapper-v18-mixed.tar.gzEnvironment variables (model gateways / key pool / budget): docker/ENV.md.
Single-graph operation, the fact-intent CLI, and multi-challenge sweeps: see SKILL.md.
Fine offensive tools from past seasons have been abused. Script kiddies used to be limited in the harm they could do; with AI, the same open-source tools in the wrong hands scale that harm enormously — with great power comes great responsibility.
We propose a taggant mechanism: markings strongly coupled into an open-source offensive harness — harmless during authorized testing (nobody cares if their IP and username leak during legal work), but during illegal intrusions the information leaks to the victim and supports attribution. Adversarial assumptions (expert stripping, framing) call for cryptographic design, which needs community co-building — our own capacity is limited, so join us; we may try to include it in a future release.
- Taggant co-building: decentralized signatures / redundant multi-channel marking / zero-key verification for defenders — discuss in issues
- More arenas: the Nüwa foundation is not pentest-specific. Offices, research, any long-horizon cooperative work — iterate on it and see what civilization grows. The next time an ape looks up at the stars, let it be yours.
Thanks to Cairn by Lèxiào — when we needed a way to record history for AI agents, their approach was a great reference; thanks to TSecBench and GeekCon for the battlegrounds.
Author: N1nEmAn · VarFusion Lab ｜ WeChat: 未知聚变 (VarFusion)
License: MIT (plus DISCLAIMER.md)