argus-ai-scanner 1.2.1

AI-native code security scanner with cascade analysis and Firecracker-microVM DAST runtime validation

Argus Scanner

We don't flag what we can't exploit.

Argus is an AI-native code security scanner combining a cost-tiered LLM harness (Gemini Flash-Lite triage → Sonnet 4.6 → Opus 4.6 escalation) with runtime DAST detonation in a Firecracker microVM and sandbox-verified remediation. Whether the bug is in code your team wrote (SQL injection, auth bypass, deserialization, command injection, crypto misuse) or in code your stack quietly pulled in (a malicious package, a poisoned CLAUDE.md, a backdoored setup.py, a tampered ML checkpoint loader about to run on someone's machine) — Argus detonates it in the sandbox, captures the exploit firing, generates a patch, replays the same exploit against the patched source, and ships the result as a CI gate.

It targets the gap between "this looks suspicious" (pattern-matching SAST) and "this actually exploits something" (manual reverse engineering).

One scanner. Two threat models. Zero false-positive triage.

Open source. BYOK. Apache 2.0.

You pay your providers directly — Anthropic + Google for the cascade, Fly.io for the optional DAST sandbox. Argus collects nothing.

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Quick Start

Get from install to first scan in under 60 seconds:

pip install argus-ai-scanner
export ANTHROPIC_API_KEY="your-anthropic-key"
export GEMINI_API_KEY="your-gemini-key"

# Single file
argus scan path/to/suspicious.py

# Whole repo (current directory)
argus scan-repo .

# CI mode — only files changed vs main, SARIF for GitHub Code Scanning
argus scan-repo . --diff origin/main --output sarif --output-file findings.sarif

Without DAST configured the CLI gracefully degrades to cascade-only verdicts. DAST mode (Firecracker sandbox) requires a Fly.io account — see docs/dast-setup.md.

Benchmark Performance

Adversarial regression suite, labeled by a 4-LLM consensus oracle. Methodology, sample size, and per-file breakdown: bench_results/v1_1_launch/launch_report.md.

                       Verdict-exact (higher = better)
Argus (cascade + DAST) ████████████████████  91.3%
Gemini 3.1 Pro         █████████████████░░░  82.6%
Grok 4.3               █████████████████░░░  82.6%
Opus 4.6               █████████████████░░░  78.3%
GPT 5.4                ████████████████░░░░  73.9%

How Argus works (the three pillars)

Argus has three pillars. The capability matrix below shows exactly what each pillar does for each file type.

Pillar 1 — Cascade harness (static + AI analysis)

Every recognized file flows through a cost-tiered model cascade. Deterministic preprocessing first (free, no models): SHA-256, multi-stage deobfuscation (base64 / hex / eval-chain), dependency graphing, attack-vector flagging, AI-file-pattern detection. Files with no outbound intent get dropped before a single token is spent.

Survivors route through a model cascade:

Cascade stage	Model	Cost / file	Decides
Triage	Gemini Flash-Lite	~$0.001	CLEAN / LOW / HIGH routing
Cheap analysis (LOW tier)	Gemini Flash	~$0.02	findings on low-priority files
Default deep analysis (HIGH tier)	Anthropic Claude Sonnet 4.6	~$0.07	findings on high-priority files
High-stakes / borderline escalation	Anthropic Claude Opus 4.6	~$0.15	~20% of HIGH files

The harness emits structured findings: CWE, line, severity, code, explanation, suggested fix, proof-of-concept, behavioral profile, attack chains, composite risk score. Aggregate cost is ~$4.65 per 100-file scan on a realistic workload mix; hard per-file + per-scan cost caps abort runs that exceed your declared budget.

Pillar 2 — DAST runtime detonation

When the harness flags suspicion at sufficient verdict tier, the file moves to a Firecracker microVM (minimal-v1, networked-v1, or ml_tools-v1 image profile) for two phases:

• Phase A — exploit testing. Plan an exploit per harness finding, run it in the sandbox, capture syscalls / egress / filesystem writes, classify each finding as CONFIRMED / BLOCKED / UNREACHED / NOT_TESTED based on what actually happened.
• Phase B — exploit discovery. Given accumulated evidence, propose NEW hypotheses the harness missed. A deterministic validator gates the proposals; survivors carry forward into the next iteration's Phase A. Up to 3 iterations or until convergence.

This is the layer that kills false positives — a "looks like SQL injection" pattern that the file's own escaping defends against gets BLOCKED, not flagged. And it surfaces what static analysis missed — Phase B has actually found new findings the harness didn't catch.

Pillar 3 — Remediation (fix-and-verify)

When Phase A confirms an exploit on text source (Python, JS / TS, shell), Argus generates a patched version, replays the same exploit attempts against the patched code in the same sandbox, and emits per-finding NEUTRALIZED / STILL_EXPLOITABLE / UNVERIFIABLE with sandbox-grounded evidence. You don't get a remediation suggestion; you get a remediation that's been tested.

Binary artifact policy. For ML artifacts (.pkl / .pt / .bin / .safetensors / .h5 / .onnx), Argus does NOT auto-patch the binary — the model can't emit valid bytecode-level patches and a corrupt patched pickle would mislead the replay. Instead, the remediation pillar emits structured guidance: regenerate the model from a clean training pipeline and serialize using safetensors (which is structurally incapable of carrying executable __reduce__ payloads). Status is UNVERIFIABLE with the guidance in fix_summary.

Opt-out: pass --no-remediation to skip this pillar entirely while keeping the harness + DAST active. Use for compliance scans, CI gates that don't allow source-modification suggestions, read-only audits, or to save ~$0.05/file in patch-generation tokens. The result still includes a structured phase_c block with skipped_reason: "phase_c_disabled_by_config" so downstream consumers can distinguish "remediation off" from "ran and found nothing to fix."

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Coverage matrix

What each pillar does, per file type. ✅ = supported, ⚠️ = supported with policy nuance, ⏳ = roadmap, ❌ N/A = not applicable to this format.

File type	Harness analysis	DAST exploit testing	DAST exploit discovery	Remediation
Python (.py, .pyw, .pyi, .pth)	✅	✅	✅	✅ patch + replay
JavaScript / TypeScript (.js, .mjs, .cjs, .jsx, .ts, .tsx)	✅	✅	✅	✅ patch + replay
Shell (.sh, .bash, .zsh)	✅	✅	✅	✅ patch + replay
Jupyter notebooks (.ipynb)	✅ cell-by-cell decomposition	⏳ roadmap	⏳ roadmap	⏳ roadmap
ML model artifacts (.pkl, .pickle, .pt, .bin, .safetensors, .h5, .hdf5, .keras, .onnx)	✅ pickletools disassembly	✅ load-detonation in sandbox	❌	⚠️ guidance only (no auto-patch — see binary policy)
GitHub Actions workflows (.github/workflows/*.yml)	✅ deterministic CI-pattern sweep	⏳ roadmap	⏳ roadmap	⏳ roadmap
Supply-chain manifests (package.json, requirements.txt, Cargo.lock, go.mod, Gemfile, Pipfile, setup.py, pyproject.toml, pom.xml, build.gradle, *.csproj, etc.)	✅ parsed for deps + lifecycle hooks	❌ N/A (no runtime to detonate)	❌ N/A	❌ N/A
AI-agent config sentinels (CLAUDE.md, AGENTS.md, SKILL.md, .cursorrules, .clinerules, mcp.json, plugin.json, openapi.{yaml,json}, agent-config.{yaml,json,toml}, etc.)	✅ prompt-injection surface	❌ N/A	❌ N/A	❌ N/A
Other languages tagged for harness (Java, Kotlin, Scala, Go, Rust, Ruby, PHP, C#, C/C++, PowerShell, Lua, Perl, R, Swift, Terraform, HCL)	✅ generic harness analysis	⏳ roadmap	⏳ roadmap	⏳ roadmap

Per-finding verdicts (where the FP kill happens)

Every finding ships with one of these statuses:

Status	Meaning
CONFIRMED	Sandbox observed the exploit firing. PoC + event trace surfaced with the finding.
BLOCKED	Attack was tested; the file's own code defended against it (sanitization, escaping, allowlist).
UNREACHED	Attack was tested; the code path is genuinely unreachable.
NOT_TESTED	Sandbox couldn't execute the test. Sub-reason: infra_stub / inconclusive / not_planned.

A CONFIRMED finding looks like this:

{
  "cwe": "CWE-200",
  "type": "data_exfiltration",
  "severity": "critical",
  "status": "CONFIRMED",
  "confidence": 1.0,
  "runtime_evidence": "Mock HTTP server at 127.0.0.1:8000 captured POST body containing 'FAKE_PRIVATE_KEY_CONTENT' and 'ssh-rsa AAAAFAKEKEY user@host'. The malware decoded its base64 payload and POSTed the contents of ~/.ssh/ to the rewritten C2 endpoint.",
  "proof_of_concept": "On any Unix host with SSH keys present, execution sends the full contents of ~/.ssh/ to the remote C2 server over HTTPS."
}

DAST cuts three ways: it confirms exploits with sandbox-captured evidence, refutes false positives with proof of non-exploitability, and verifies remediations by replaying the same exploits against the patched source.

Enterprise Invariants

Anthropic's Claude Security and OpenAI's Codex Security are enterprise-tier and vendor-cloud-only. Argus is the open alternative.

• BYOK. You control LLM access; bills go to your API meter, not ours.
• Zero telemetry. In cascade-only mode, nothing leaves your machine. In DAST mode, file content is sent only to a Fly.io app you own and control — never to Argus-operated infrastructure.
• Local execution. Fully self-contained pipeline; no SaaS dependency.

CLI Reference

argus scan <file> — single-file scan

Flag	Purpose
--output {json,markdown}	Output format (default: json)
--no-dast	Skip DAST verification (cascade-only)
--no-remediation	Skip Phase C (fix-and-verify). Phase A + B still run; no patch is generated. Compliance / CI-gate / read-only-audit use cases. Saves ~$0.05/file.
--max-cost USD	Abort this file's scan if per-file API spend exceeds USD (default: $1.00; pass 0 to disable)
--enable-discovery	Proactive payload sweep — runs library of attack payloads against the file in sandbox; surfaces runtime-confirmed CWEs as new findings (+~$0.25/file)
--dast-trigger-verdicts LIST	Comma-separated L1 verdicts that trigger DAST. Default: malicious,critical_malicious. Allowed: clean,suspicious,malicious,critical_malicious

argus scan-repo <path> — directory tree scan

Flag	Purpose
--diff REF	Only scan files differing vs git ref (e.g., --diff origin/main for PR/CI)
--output {markdown,json,sarif}	Output format (default: markdown); sarif is SARIF v2.1.0 for GitHub Code Scanning
--output-file PATH	Write to file instead of stdout
--max-cost USD	Abort the run when cumulative API spend across all files exceeds USD; remaining files are marked cost_cap_reached. Pass 0 or omit to disable
--exclude GLOB	Additional gitignore-style exclude pattern (repeatable)
--no-gitignore	Ignore .gitignore during walk (default: respected)
--max-file-bytes BYTES	Skip files larger than BYTES (default: 1 MiB)
--no-dast	Skip DAST verification on every file
--no-remediation	Skip Phase C on every file. Phase A + B still run; no patches generated.
--enable-discovery	Proactive payload sweep on every DAST-eligible file
--dast-trigger-verdicts LIST	Same as scan
--continue-on-error / --no-continue-on-error	On per-file exception, record and continue (default) or abort run

argus bench — re-run the benchmark

Flag	Purpose
--suite PATH	Regression-suite directory (default: samples/regression_v1)
--n N	Runs per config (default: 2)
--no-dast	Run Argus pipeline L1-only (compare L1-vs-Opus separately)
--dry-run	Print cost projection without making model calls
--yes / -y	Skip cost-projection confirmation prompt

Security & Isolation

Argus deliberately detonates potentially malicious code. Host protection is non-negotiable.

• Hardware-level isolation. Execution happens inside Firecracker microVMs using KVM hardware virtualization.
• Ephemeral state. Every detonation spins up a pristine microVM and is destroyed post-execution. Zero persistence.
• Strict egress control. Network profiles enforced at the hypervisor level prevent lateral movement during DAST verification.

Documentation

Topic	Page
Install guide	docs/install.md
API key sourcing	docs/api-keys.md
Architecture deep dive	docs/architecture.md
DAST sandbox setup	docs/dast-setup.md
Cost guide	docs/cost-guide.md
Roadmap	ROADMAP.md
Contributing	CONTRIBUTING.md
Security disclosures	SECURITY.md

License

Apache License 2.0.