argus-ai-scanner

v1.12.0 suspicious
6.0
Medium Risk

AI-native code security scanner: runtime sandbox-validated remediation. Confirm exploits, auto-patch, replay against the fix — all at machine scale.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential obfuscation and attempts to access sensitive information, raising concerns about its legitimacy and intentions.

  • High obfuscation risk through base64 and gzip
  • Attempt to access '../../../etc/passwd' indicating potential privilege escalation
Per-check LLM notes
  • Obfuscation: The use of base64 and gzip for decompression suggests potential obfuscation to hide code logic, which is suspicious.
  • Credentials: Accessing environment variables like GITHUB_TOKEN could be legitimate, but the attempt to read '../../../etc/passwd' indicates an effort to escalate privileges or access sensitive information.
  • Metadata: Low risk due to lack of suspicious flags, but author details are incomplete and the maintainer has a new or inactive account.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/dshochat/Argus_Scanner#readme
  • Detailed PyPI description (8957 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
  • 402 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 79 commits in dshochat/Argus_Scanner
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • lee. e.g., ``urlopen``, ``urllib.request.urlopen``, ``self.client.fetch``.""" line_number: int =
  • try: resp = requests.get( f"{self.api_base}{path}", h
  • try: resp = requests.post( f"{self.api_base}{path}", h
  • e | None] = {} async with httpx.AsyncClient() as client: async def _dl(repo: str, ref: str) ->
  • ne client = client or httpx.AsyncClient() all_items: dict[str, dict[str, Any]] = {} # dedup
  • """ async with httpx.AsyncClient() as client: # Phase A: discover target repos.
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • content = gzip.decompress(base64.b64decode(file_b64)) target_path.write_bytes(content)
  • # # The 23-file adjudication eval (Gemini 3.1 Pro on 18 W1 candidates) # found 8/16 over-claims
  • pressions. If the file does ``eval(input)`` or ``exec(input)`` # or imports based on in
  • ction — Python ``exec()`` / ``eval()`` runs in-process bytecode; no execve fires for the le
  • "content": "{\\"hook\\": \\"__import__('os').system('id')\\"}"}, {"op": "call", "function_name
  • payload_input=( f"__import__('urllib.request').request.urlopen(" f"'http://{_DISCOVERY_DOMAIN}
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • **"loading this file caused ``os.system('echo pwned')`` to actually fire in the sandbox."** Plans b
  • ayload. Side-effect via ``os.system('touch /tmp/argus_probe_pwned')`` so Rule 2 (canary tmp
  • l'd: try ``\"; import os; os.system('touch /tmp/argus_probe_pwned'); \"``.""" return "'; __i
  • bytes that, when loaded, run `os.system('touch ...')`. Base64-encoded so it survives JSON-in-JSO
  • try: proc = subprocess.run( cmd, shell=True,
  • lative paths sane. proc = subprocess.Popen( shlex.split(launch_command, posix=os.name != "nt"),
⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • unusable).""" token = os.environ.get("GITHUB_TOKEN", "").strip() if not token: raise GitHu
  • rmissionError on '../../../etc/passwd'" pattern).""" judge_reasoning: str = "" """v1.8 S
  • d be a pathological ``../../../etc/passwd``). try: target.relative_to(self.root.r
  • _file() would attempt to read /etc/passwd via # network, sqlite's attach() also gives a signa
  • payload_input="../../../etc/passwd", commands=( # If the file does path tr
  • no network) but # /etc/passwd content has a recognisable root: header. We # c
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository dshochat/Argus_Scanner appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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-ai-scanner 
Develop a fully-functional mini-application named 'CodeGuard' that leverages the 'argus-ai-scanner' package to enhance code security. CodeGuard should perform the following tasks:

1. **Initialization**: Start by setting up a command-line interface (CLI) that allows users to input a path to a directory containing source code files.
2. **Scanning**: Utilize the 'argus-ai-scanner' to scan the specified directory for potential security vulnerabilities. Ensure that the scanning process is comprehensive, covering various types of codebases (e.g., Python, JavaScript).
3. **Exploit Confirmation**: Once vulnerabilities are detected, CodeGuard should simulate an attack to confirm if these vulnerabilities can indeed be exploited. This step ensures that only real threats are flagged.
4. **Auto-Patching**: For confirmed vulnerabilities, CodeGuard should automatically generate and apply patches to the affected code. This feature should be smart enough to suggest multiple patch options where applicable.
5. **Replay Testing**: After applying the patches, CodeGuard should re-run the exploit simulation to verify that the vulnerabilities have been successfully mitigated.
6. **Reporting**: Provide a detailed report summarizing the findings, including the nature of the vulnerabilities, the patches applied, and the results of the post-patch testing.
7. **User Interface**: Enhance the CLI with a simple text-based UI that clearly displays the status of the scans, vulnerabilities found, and actions taken. Additionally, include options for exporting the report in PDF or CSV format.
8. **Machine-Scale Operations**: CodeGuard should be scalable to handle large codebases efficiently, thanks to the machine-scale capabilities of 'argus-ai-scanner'.

Suggested Features:
- Integration with popular version control systems like Git to track changes before and after patching.
- Support for custom rulesets that allow users to define their own criteria for vulnerability detection.
- A feature to schedule regular scans for continuous security monitoring.
- An option to send alerts via email or webhook when vulnerabilities are detected or fixed.

How 'argus-ai-scanner' is Utilized:
- The package is primarily used during the scanning phase to identify potential vulnerabilities. It employs advanced AI techniques to understand the context of the code and detect anomalies that could pose security risks.
- During the exploit confirmation phase, 'argus-ai-scanner' provides a sandbox environment to safely simulate attacks and validate the presence of vulnerabilities.
- In the auto-patching phase, the package's intelligent algorithms help in generating effective fixes that not only address the immediate issue but also improve overall code quality.
- Finally, the replay testing phase relies on 'argus-ai-scanner' to ensure that the applied patches effectively eliminate the vulnerabilities, providing peace of mind to developers.

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