🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several suspicious characteristics including potential obfuscation techniques and low maintainer activity, which raises concerns about its integrity and purpose.

Obfuscation risk due to base64 decoding and uuid generation
Low maintainer activity and poor metadata quality

Per-check LLM notes

Network: Network calls are likely for legitimate purposes such as API interactions or updates, but further investigation into the endpoints is advised.
Shell: Shell execution patterns may be for git operations related to version control, but could also indicate unintended system interaction. Context about the package's purpose is needed.
Obfuscation: The use of base64 decoding and uuid generation might indicate an attempt to obscure code logic, raising suspicion.
Credentials: No clear signs of credential harvesting detected, but further investigation is advised.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

try: with urllib.request.urlopen(base + path, timeout=8) as resp: if
dy).encode("utf-8") req = urllib.request.Request(url, data=data, method="POST") req.add_header("C
, f"Bearer {token}") with urllib.request.urlopen(req, timeout=timeout) as resp: return json.l
-> dict[str, Any]: req = urllib.request.Request(url, method="GET") if token: req.add_hea
int = 60) -> bytes: req = urllib.request.Request(url, method="GET") if token: req.add_hea

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

raw = base64.b64decode(item["content_b64"]) except Exception as
led": True, "install_id": str(__import__("uuid").uuid4())}})) spy = mock.Mock() monkeypatch.setattr

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

ne: try: result = subprocess.run( ["git", *args], capture_output=True
-> bool: try: r = subprocess.run( ["git", "-C", cwd, "remote", "get-url", "origin
try: r = subprocess.run( ["git", "-C", cwd, *args],
""" try: result = subprocess.run( ["git", *args], capture_output=True
| None: try: r = subprocess.run( ["git", *args], capture_output=True
try: r = subprocess.run( [ "git",

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agent-trace-cli

Develop a Python-based application named 'CodeTrace' that leverages the 'agent-trace-cli' package to trace modifications made by AI agents in a software development environment. This tool will serve as a valuable resource for developers to monitor and understand the impact of AI-driven code changes on their projects. The application should have the following core functionalities:

1. **AI Code Change Detection**: Implement a feature that integrates with the 'agent-trace-cli' to detect any changes made by AI agents in the codebase. This involves setting up hooks or listeners that trigger whenever an AI agent modifies the code.

2. **Change Impact Analysis**: Once changes are detected, the application should analyze the impact of these changes. This includes identifying which parts of the code were affected, whether the changes introduced new bugs, improved performance, or altered functionality.

3. **Visualization of Traces**: Provide a user-friendly interface where developers can visualize the traces of AI-generated changes. This could include timelines showing when changes occurred, pie charts displaying the distribution of changes across different modules, and heatmaps indicating areas of high activity or conflict.

4. **Detailed Reports**: Generate comprehensive reports summarizing the analysis performed by the application. These reports should include details such as the type of changes made, the rationale behind each change (if provided by the AI), and any recommendations for further action based on the analysis.

5. **Integration with Version Control Systems**: Ensure that 'CodeTrace' can seamlessly integrate with popular version control systems like Git to track changes over time and correlate them with commit messages and other metadata.

6. **Customizable Alerts**: Allow users to set up alerts for specific types of changes or events. For example, they might want to be notified if an AI agent introduces a significant number of changes in a short period or if certain critical files are modified.

To utilize the 'agent-trace-cli' package effectively, your application should call its functions at strategic points in the workflow, such as during pre-commit hooks, post-commit hooks, or periodic audits of the codebase. Additionally, consider exploring how you can enhance the package's capabilities through custom scripts or plugins developed within 'CodeTrace'.

Your goal is to create a robust, user-friendly tool that not only tracks AI-generated code changes but also provides actionable insights to help developers manage and optimize their development processes.