🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits elevated risks due to its use of subprocess calls and eval function, indicating potential for code injection and unauthorized actions. While there's no definitive evidence of malicious intent, the combination of these factors warrants caution.

High shell risk due to subprocess.run usage
Obfuscation risk from eval function

Per-check LLM notes

Network: Network calls may be part of legitimate functionality, but the incomplete snippet raises suspicion about intent.
Shell: Use of subprocess.run to execute shell commands like 'curl' and 'git' can indicate potential for remote command execution or updates, suggesting elevated risk.
Obfuscation: The use of 'eval' with an external source (osidb_cache) suggests potential for code injection and should be reviewed carefully.
Credentials: No clear signs of credential harvesting detected.
Metadata: The maintainer has a new or inactive account and lacks a proper author name, which could indicate potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

/')}" self._session = requests.Session() self._session.headers.update(default_tool_http_hea
("/") self._session = requests.Session() self._session.headers.update(default_tool_http_hea
ist[str] = [] async with httpx.AsyncClient(timeout=30.0) as client: for i, h in enumerate(to_fe
self.http_client = httpx.AsyncClient( timeout=httpx.Timeout(timeout),
tches = [] async with httpx.AsyncClient(timeout=30.0) as client: for commit_hash in comm
str]] = [] async with httpx.AsyncClient(timeout=30.0) as client: for commit_hash in comm

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

is-371 component intelligence eval (evals/osidb_cache). # Used when AEGIS_EVALS_SUGGEST_AFFECTED

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

es" try: result = subprocess.run( ["curl", "-s", "--negotiate", "-u", ":", url],
try: subprocess.run( [ "git"
try: subprocess.run( ["git", "pull"],
ext. """ try: subprocess.run(["git", "--version"], check=True, capture_output=True) e

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: redhat.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository RedHatProductSecurity/aegis-ai 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 aegis-ai

Your task is to develop a Python-based security analysis tool named 'AegisGuard', which will leverage the functionalities of the 'aegis-ai' package to perform comprehensive security assessments on various security artifacts such as network configurations, firewall rules, and system logs. This tool aims to automate the process of identifying potential vulnerabilities and providing actionable insights to improve overall cybersecurity posture.

### Project Overview:
- **Tool Name:** AegisGuard
- **Primary Functionality:** Automated security analysis using 'aegis-ai'
- **Target Audience:** IT Security Professionals, System Administrators

### Core Features:
1. **Artifact Upload:** Users should be able to upload different types of security artifacts (e.g., .txt files containing firewall rules, .csv files with network configurations).
2. **Security Analysis:** Utilize 'aegis-ai' to analyze the uploaded artifacts for common security issues, compliance violations, and best practices deviations.
3. **Report Generation:** Generate detailed reports summarizing the findings from the security analysis, including recommendations for remediation.
4. **User Interface:** Develop a simple command-line interface (CLI) for ease of use.
5. **Customization Options:** Allow users to specify certain parameters for the analysis (e.g., specific compliance standards to check against).
6. **Real-Time Feedback:** Provide real-time feedback during the analysis process to indicate progress and any immediate issues found.

### Detailed Steps:
1. **Setup Environment:** Begin by setting up a Python virtual environment and installing necessary packages including 'aegis-ai'.
2. **Artifact Handling:** Implement functionality to accept and validate different types of security artifact uploads. Ensure the tool can handle multiple file formats and sizes efficiently.
3. **Integration with 'aegis-ai':** Use 'aegis-ai' to analyze each artifact according to its type. For instance, if a firewall rule file is uploaded, ensure 'aegis-ai' performs relevant checks against known vulnerabilities and best practices.
4. **Generate Reports:** Based on the analysis performed by 'aegis-ai', generate a comprehensive report that includes sections like summary of findings, detailed analysis, and recommended actions.
5. **Develop CLI:** Design a user-friendly CLI that guides users through the process of uploading artifacts, selecting analysis options, and viewing results.
6. **Testing & Validation:** Rigorously test AegisGuard with a variety of security artifacts to ensure it accurately identifies vulnerabilities and provides useful recommendations.
7. **Documentation:** Create thorough documentation explaining how to install, configure, and use AegisGuard effectively.

By completing these steps, you will have developed a powerful yet accessible tool for enhancing cybersecurity through automated security analysis.