agent_sre

v3.7.0 suspicious
6.0
Medium Risk

Public Preview — Reliability Engineering for AI Agent Systems

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential misuse, particularly due to obfuscation techniques and unclear credential handling, while other risks are relatively low.

  • High obfuscation risk due to base64 decoding and eval function.
  • Unclear purpose of reading from /etc/passwd
Per-check LLM notes
  • Network: The use of urllib to make network requests might be legitimate if the package is designed to send alerts or communicate with external services.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: The presence of base64 decoding and eval function suggests potential for code execution from encoded strings, which is often used maliciously.
  • Credentials: Reading from /etc/passwd without a clear purpose may indicate an attempt to harvest system credentials, but could also be part of a legitimate security test.
  • Metadata: The maintainer has a new or inactive account with minimal package history and an incomplete author profile.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • te(headers) req = urllib.request.Request( # noqa: S310 — alert webhook URL from configuratio
  • ) with urllib.request.urlopen(req, timeout=10) as resp: # noqa: S310 — alert webh
⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • CTION, # The literal "eval(" / "base64decode(" / "import os" tokens here # are
  • payload={"command": "eval(base64decode('aW1wb3J0IG9z'))"}, # noqa: S307 expec
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • SE, payload={"path": "/etc/passwd", "action": "read"}, expected_outcome="blocked",
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/agent-governance-toolkit 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 agent_sre 
Your task is to develop a reliability engineering dashboard for monitoring and managing AI agent systems using the 'agent_sre' Python package. This mini-app will serve as a critical tool for developers and reliability engineers who wish to ensure the robustness and stability of their AI agent deployments. The app will have several key functionalities that leverage the core features of the 'agent_sre' package, including but not limited to failure prediction, performance metrics analysis, and incident response automation.

**Step 1:** Begin by setting up your development environment. Ensure you have Python installed along with any necessary dependencies, including 'agent_sre'. You can install it via pip if it's not already available.

**Step 2:** Design the architecture of your mini-app. It should include components for data collection from AI agents, real-time monitoring dashboards, and automated reporting mechanisms.

**Step 3:** Implement data collection from AI agents using 'agent_sre'. Use its capabilities to gather operational data such as system logs, performance metrics, and user interaction data. Ensure this data is stored securely and efficiently.

**Step 4:** Develop real-time monitoring dashboards that visualize the collected data. Utilize 'agent_sre' features to identify potential issues before they become critical failures. The dashboard should provide insights into system health, performance trends, and anomaly detection.

**Step 5:** Integrate automated reporting and alerting systems. Configure these systems to notify relevant stakeholders about incidents or predicted failures based on data analyzed by 'agent_sre'.

**Suggested Features:**
- **Health Checks:** Regularly scheduled checks to assess the health of AI agents.
- **Predictive Maintenance:** Use machine learning models provided by 'agent_sre' to predict future failures and recommend preemptive actions.
- **Incident Management:** Automate incident documentation, root cause analysis, and post-mortem reports.
- **User-Friendly Interface:** A clean, intuitive UI for both technical and non-technical users to understand the state of their AI systems.

**How 'agent_sre' is Utilized:** Throughout the development process, rely heavily on 'agent_sre' for its advanced reliability engineering tools and methodologies. Use its built-in functions for data aggregation, anomaly detection, and predictive analytics to enhance the functionality and effectiveness of your mini-app.