argus-appsec 0.6.1

An open-source AI Security Engineer: maps your application, finds vulnerabilities, explains them, and proposes verified fixes.

Argus

An open-source AI Security Engineer. Point it at a codebase or a running application; it maps the system, runs layered security analysis, explains every finding the way a senior application-security engineer would, and — where possible — proposes and verifies a fix.

Argus is not just another scanner that prints a list. For each finding it tells you why it is a vulnerability, how an attacker would exploit it, the business impact, the likelihood and severity, the CWE/OWASP mapping, and concrete remediation — and it can generate a patch and check that the patch closes the issue.

Status: early alpha. The architecture and core pipeline are in place with a working CLI, five built-in scanners, a multi-provider AI layer, and five report formats. See the roadmap.

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Highlights

• Understands the project first. Detects languages and frameworks and builds an architecture map — APIs, auth flows, datastores, third-party services, cloud, containers, CI/CD, and dependency manifests — before scanning.
• Layered analysis. Secret detection, dependency vulnerabilities — including transitive dependencies from lock files across PyPI (poetry.lock, Pipfile.lock), npm (package-lock.json, yarn.lock), Go (go.mod), Rust (Cargo.lock), Ruby (Gemfile.lock), and PHP (composer.lock), checked against the live OSV database — static code analysis (SAST), and infrastructure-as-code checks out of the box, extensible via plugins.
• CI-native. Deterministic, reproducible output; baseline / diff-aware scanning so pull requests are gated only on newly introduced findings.
• Findings that teach. Every finding carries reasoning, taxonomy mappings, and remediation — not just a line number.
• Attack Simulation Mode. Instead of "this is vulnerable", Argus produces a safe, isolated walkthrough: how the flaw is discovered, a step-by-step (non-weaponized) exploit, the data at risk, the business impact, and how the fix blocks the attack — with a before/after comparison.
• Bring your own model. Offline heuristic provider by default (no key, no network); Anthropic and OpenAI for cloud models; Ollama for fully local models so source never leaves your environment.
• Plugin-based throughout. Scanners, reporters, and AI providers are plugins. Add a language or a report format without touching the core.
• CI-ready output. JSON, SARIF (GitHub Code Scanning), Markdown, HTML, and CSV.

Install

# From PyPI
pip install argus-appsec

# With cloud model support
pip install "argus-appsec[anthropic,openai]"

# With the AST data-flow tier (deeper Python taint analysis, via tree-sitter)
pip install "argus-appsec[ast]"

From source (for development):

git clone https://github.com/hasipfaruk/Argus
cd Argus
pip install -e ".[dev]"

Requires Python 3.10+. The command installed is argus.

Quick start

# Scan a local project and print a table
argus scan ./my-app

# Turn on the flagship features and write an HTML report
argus scan ./my-app --attack-sim --patches -f html -o report.html

# Scan a remote repository (shallow-cloned to a temp dir, then cleaned up)
argus scan https://github.com/org/repo

# Apply Argus's verified fixes to a branch and open a pull request
argus fix ./my-app --open-pr

# Use a local model so code stays on your machine
argus scan ./my-app --ai-provider ollama --ai-model llama3.1

# Machine-readable output for CI, failing the build on High+
argus scan ./my-app -f sarif -o results.sarif --fail-on high

Explore what's available:

argus scanners     # list scanners
argus reporters    # list report formats
argus providers    # list AI providers and whether each is usable right now
argus init         # write a starter .argus.yml

How it works

target ──▶ resolve ──▶ analyze ──▶ scan ──▶ enrich (agents) ──▶ report
          (path/git/    (languages,  (secrets,   (reasoning,        (json, sarif,
           url)          frameworks,  deps,        attack sim,        markdown,
                         architecture) sast, iac)  patches)           html, csv)

The engine is synchronous and side-effect free apart from reading the target: it returns a ScanResult and writes nothing. Reporting and any PR creation are separate, explicit steps. See docs/architecture.md.

Configuration

Drop a .argus.yml in your project root (generate one with argus init):

min_severity: low
fail_on: high
attack_simulation: true
generate_patches: true
ai:
  provider: ollama      # heuristic | anthropic | openai | ollama
  model: llama3.1
scanner_options:
  secrets:
    entropy_threshold: 4.2

Full reference: docs/configuration.md.

Extending Argus

Everything is a plugin. A minimal scanner:

from argus.core.plugin import Scanner, ScannerContext, scanner
from argus.core.models import Finding, Location, Severity

@scanner
class HelloScanner(Scanner):
    name = "hello"
    category = "example"
    description = "Flags TODO comments as a demo."

    def scan(self, ctx: ScannerContext):
        for f in ctx.project.files():
            for i, line in enumerate(f.lines(), 1):
                if "TODO" in line:
                    yield Finding(
                        id=f"hello:todo:{i}", rule_id="hello.todo", scanner=self.name,
                        title="TODO left in code", description="A TODO marker.",
                        location=Location(path=f.rel_path, start_line=i),
                        severity=Severity.INFO,
                    )

Register it via the argus.plugins entry point in your package and it is picked up automatically. Full guide: docs/plugins.md.

AI providers and data handling

Provider	Location	Source leaves your machine?	Needs
heuristic	local	no	nothing (default)
ollama	local	no	a running Ollama server
anthropic	cloud	yes	ANTHROPIC_API_KEY + [anthropic] extra
openai	cloud	yes	OPENAI_API_KEY + [openai] extra

If a requested provider is unavailable, Argus warns and falls back to heuristic so a scan always completes.

Fixing, not just finding

argus fix closes the loop: it scans a repository, applies the fixes it can verify locally to a fresh branch, commits them, and (with --open-pr) opens a pull request.

argus fix ./my-app --dry-run      # preview the changes, write nothing
argus fix ./my-app                # apply fixes to a branch and commit locally
argus fix ./my-app --open-pr      # also push and open a PR (needs GITHUB_TOKEN)

Only deterministic, self-verified fixes are applied by default (e.g. unsafe yaml.load → yaml.safe_load, weak hashes → SHA-256, shell=True removal, debug=True → debug=False). See docs/fixing.md.

Roadmap

Implemented: project analysis, secrets/dependency/SAST/IaC scanners, live OSV vulnerability data, multi-provider AI enrichment, Attack Simulation Mode, deterministic patch generation with self-verification, automated fix branches and pull requests (argus fix), baseline / diff-aware scanning, and JSON/SARIF/Markdown/HTML/CSV reporting.

In progress: AST-based data-flow scanning — a tree-sitter taint analyzer for Python ships today (pip install "argus-appsec[ast]"); more languages next.

Planned: dynamic analysis (DAST) for deployed URLs, the web dashboard (trends, timelines, collaboration), and richer compliance rule packs.

Known limitations

Argus is an early-stage tool and honest about what it does not yet do — a security scanner you can't trust is worse than none:

• Static analysis only. It reads code; it does not run your app or test a live URL (no DAST yet).
• Deepest for Python and JavaScript/TypeScript. Other languages are detected and covered by the secrets, dependency, and IaC scanners, but have limited code-level (SAST) rules so far.
• Code scanning has blind spots. The default regex tier catches common patterns; the optional AST tier ([ast], Python) follows multi-hop data flows. But cross-file and cross-function flows can still be missed. Treat a clean result as "no known issues found," not a proof of safety.
• Dependency coverage depends on OSV. Offline, it falls back to a small bundled advisory seed (PyPI/npm) and will say so.

Treat Argus as a strong, fast first line of defense — not a replacement for a manual security review of critical code.

Contributing

Contributions are welcome from everyone. The flow is the standard one:

1. Fork this repository.
2. Create a branch and make your change (with tests).
3. Open a pull request — the template will guide you, and CI runs tests and lint automatically.

Scanners, language support, compliance rules, and report formats are especially welcome — the plugin model means most additions never touch the core. See CONTRIBUTING.md for setup and standards, and CODE_OF_CONDUCT.md for community guidelines.

License

Apache-2.0. See LICENSE.