argus-security

v1.3.1 suspicious
4.0
Medium Risk

Unified security scanning — SAST, containers, IaC, secrets, dependencies, and DAST from a single CLI.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package presents a moderate risk due to the high credential risk score despite no clear signs of malicious intent or supply-chain attack. Further investigation into the usage of environment variables for sensitive data is recommended.

  • High credential risk due to potential harvesting of sensitive tokens
  • No significant obfuscation or metadata red flags
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: Potential risk of credential harvesting as sensitive tokens are being accessed from environment variables.
  • Metadata: The maintainer has only one package, which could indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Medium (5.8/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://huntridge-labs.github.io/argus
  • Detailed PyPI description (2987 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 345 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 5 unique contributor(s) across 100 commits in huntridge-labs/argus
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • try: with socket.create_connection((host, port), timeout=2): # TCP connected —
  • L. resp = urllib.request.urlopen( # nosec B310 f"http://{hos
  • lication/json" req = urllib.request.Request(url, data=body, headers=headers, method=method)
  • / reachable. with urllib.request.urlopen(req, timeout=15) as resp: # nosec B310
  • load else None req = urllib.request.Request(url, data=body, headers=headers, method=method)
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • e) try: result = subprocess.run( cmd, capture_output=True,
  • ue try: result = subprocess.run( ["docker", "rmi", "--force", image_ref],
  • rn try: result = subprocess.run( ["docker", "builder", "prune", "--force", "--fi
  • rn try: result = subprocess.run( ["docker", "image", "prune", "--force"],
  • se try: result = subprocess.run( ["docker", "image", "inspect", image_ref],
  • 0 try: result = subprocess.run( ["docker", "manifest", "inspect", image_ref, "-
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • r="github", api_token=os.environ.get("GITHUB_TOKEN"), repo_slug=os.environ.get("GITHUB_REPOSITORY"),
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository huntridge-labs/argus appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Huntridge Labs" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with argus-security 
Create a Python-based mini-application called 'SecurityScanTool' that leverages the 'argus-security' package to perform comprehensive security scans on a given codebase, container images, infrastructure as code files, secret detection, dependency analysis, and dynamic application security testing (DAST). This tool should be user-friendly, allowing users to select which types of scans they want to run and providing detailed reports on vulnerabilities found.

Step 1: Set up the development environment by installing Python and the 'argus-security' package.
Step 2: Design the application's command-line interface (CLI) to accept user input regarding the type of scan (SAST, container, IaC, secrets, dependencies, or DAST).
Step 3: Implement functions within the application that use 'argus-security' to execute the selected scan(s) on the provided inputs.
Step 4: Develop a reporting system that summarizes the findings of each scan in a clear and actionable format.
Step 5: Integrate an option for users to save the scan results into a file for further review or archival.

Suggested Features:
- Support for multiple input formats depending on the scan type (e.g., code directories for SAST, Dockerfile paths for container scans).
- Real-time progress updates during the scan process.
- An interactive mode where users can choose additional parameters for specific scans (such as ignoring certain rules or focusing on specific parts of the codebase).
- A summary report at the end of all scans that highlights critical issues first.

Utilizing 'argus-security':
- Use the package's unified CLI capabilities to initiate different types of scans based on user selection.
- Leverage its built-in rule sets and configurations to ensure thorough coverage across various aspects of security.
- Incorporate its output parsing functionality to extract meaningful insights from raw scan results.

💬 Discussion Feed

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