assay-ai

v1.23.0 safe
4.0
Medium Risk

AI evidence that's harder to fake and easier to verify

🤖 AI Analysis

Final verdict: SAFE

The package appears to be used for legitimate purposes with low risks associated. While there are some areas that warrant caution, such as shell execution and incomplete metadata, these do not strongly indicate malicious intent.

  • Shell risk due to git command execution
  • Incomplete maintainer metadata
Per-check LLM notes
  • Network: Fetching package metadata from PyPI is a common practice and generally safe.
  • Shell: Executing git commands suggests the package may be performing version control operations, which could be legitimate but warrants further investigation.
  • Obfuscation: The observed patterns suggest legitimate use of Base64 decoding for cryptographic operations rather than obfuscation.
  • Credentials: No suspicious patterns indicative of credential harvesting were found.
  • Metadata: The maintainer has incomplete information and a new or inactive account, which raises some suspicion but not enough to conclusively indicate malice.

📦 Package Quality Overall: Low (4.6/10)

○ 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://github.com/Haserjian/assay/blob/main/docs/README_qui
  • Detailed PyPI description (32000 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 363 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in Haserjian/assay
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • t json as _json with urllib.request.urlopen( "https://pypi.org/pypi/assay-ai/json",
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • try: sig_bytes = base64.b64decode(value) except Exception: return SigResult(
  • pubkey_bytes = base64.b64decode(signer_pubkey_b64) actual_fp = hashlib.sha25
  • vk.verify(canonical_bytes, base64.b64decode(signature_b64)) verified = True
  • anifest_bytes) sig_raw = base64.b64decode(signature_b64) (output_dir / PACKET_SIGNATURE_FILE).writ
  • bkey"] pubkey_bytes = base64.b64decode(pubkey_b64) # Verify pubkey fingerprint exp
  • ey_bytes) sig_bytes = base64.b64decode(manifest["signature"]) try: vk.verify(ca
⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • r) -> list[str]: result = subprocess.run( ["git", "-C", str(repo_root), *args], check
  • se try: result = subprocess.run( ["git", "rev-parse", "--short", "HEAD"],
  • out.strip() result = subprocess.run( ["git", "status", "--porcelain"], c
  • try: proc = subprocess.run( cmd, input=stdin_payload,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ 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 Haserjian/assay 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 assay-ai 
Create a mini-application named 'EvidenceVerifier' using the Python package 'assay-ai'. This application aims to provide users with a simple yet powerful tool to verify the authenticity of digital evidence such as images, videos, and text. The core functionality of 'EvidenceVerifier' will involve uploading a piece of digital evidence, which will then be analyzed by 'assay-ai' to determine its authenticity. The application should output a report detailing the likelihood of the evidence being authentic and any potential signs of tampering or forgery.

Steps to develop the application:
1. Set up a basic Flask web application to serve as the front-end interface for uploading evidence.
2. Integrate the 'assay-ai' package into your backend to handle the verification process of uploaded evidence.
3. Implement a user-friendly interface where users can upload their files (images, videos, text documents).
4. Use 'assay-ai' to analyze the uploaded evidence, generating a report on the evidence's authenticity.
5. Display the results back to the user in a clear, easy-to-understand format.
6. Optionally, add features like saving verification reports, comparing multiple pieces of evidence, or even sharing results via email.
7. Ensure the application is secure, handling uploads safely and protecting user data.

Suggested Features:
- Support for multiple file types (images, videos, text)
- Real-time feedback during the verification process
- Detailed reports including graphical representations of analysis
- Option to save verification reports for future reference
- Ability to compare different pieces of evidence side-by-side
- Sharing results via email or downloading the report as a PDF
- User authentication and authorization for saved reports and comparisons

How 'assay-ai' is Utilized:
- Utilize 'assay-ai' to analyze uploaded evidence, leveraging its capabilities to detect tampering, forgery, and other forms of manipulation.
- Use 'assay-ai' to generate comprehensive reports on the authenticity of each piece of evidence, providing insights into the likelihood of it being genuine.

💬 Discussion Feed

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