SLiCAP

v4.0.15 suspicious
6.0
Medium Risk

SLiCAP: more than SYMBOLIC SPICE

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation and shell execution risks, which could indicate potential security vulnerabilities or malicious intent. However, there's no clear evidence of credential harvesting or severe network activities.

  • High obfuscation risk due to use of eval
  • Potential shell command injection risk
Per-check LLM notes
  • Network: The network call appears to be checking for package updates from GitHub, which is generally benign.
  • Shell: The shell execution patterns suggest the package may be using system commands to perform tasks like exporting files, which could pose a risk if not properly sanitized or handled.
  • Obfuscation: The use of eval on configuration values may indicate an attempt to hide code logic or intentions, which is concerning for potential code injection risks.
  • Credentials: No direct evidence of credential harvesting is present, but caution is advised as the context of eval usage could potentially be exploited.
  • Metadata: The author information is incomplete, which raises some suspicion but does not strongly indicate malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: response = requests.get("https://api.github.com/repos/SLiCAP/SLiCAP_python/releases/
⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • hz = eval(project_config['display']['Hz']) disp = ev
  • z']) disp = eval(project_config['display']['digits']) scalefactors
  • s']) scalefactors = eval(project_config['display']['scalefactors']) eng_notation
  • s']) eng_notation = eval(project_config['display']['engnotation']) laplace
  • fy'] step_function = eval(project_config['math']['stepfunction']) factor
  • n']) factor = eval(project_config['math']['factor']) max_rec_subst = e
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • nds[key] found = os.system(cmd) if found == 256: commands
  • Windows': subprocess.run([ini.gnetlist, '-q', '-g', 'spice-noqsi', '-o', outputfile,
  • ','\\\\') subprocess.run([ini.gnetlist, '-q', '-g', 'spice-noqsi', '-o', outputfile,
  • try: subprocess.run([ini.lepton_eda, 'export', '-o', pdfFile, fileName], stdout=
  • s.STDOUT) subprocess.run([ini.lepton_eda, 'export', '-o', svgFile, fileName], stdout=
  • th + cirName try: subprocess.run([ini.kicad, 'sch', 'export', 'netlist', '-o', ini.cir_path +
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: montagne.nl>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository SLiCAP/SLiCAP_python 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 SLiCAP 
Create a Python-based mini-application that leverages the SLiCAP package to automate the analysis of electronic circuits described in SPICE netlists. This tool will be particularly useful for students and engineers who need to perform quick symbolic and numerical analyses on their circuit designs without manually calculating each component's behavior.

### Project Overview:
- **Name:** CircuitAnalyzer
- **Purpose:** To provide a user-friendly interface for analyzing SPICE netlists using SLiCAP's powerful capabilities.
- **Target Audience:** Engineers, researchers, and students working with electronic circuits.

### Core Features:
1. **Symbolic Analysis:** Automatically generate symbolic expressions for key circuit parameters such as voltage gain, input impedance, etc., directly from the SPICE netlist.
2. **Numerical Simulation:** Perform DC, AC, and transient simulations on the circuit based on the netlist data.
3. **Visualization:** Display simulation results graphically, including Bode plots, Nyquist diagrams, and time-domain responses.
4. **Interactive Interface:** Allow users to modify circuit parameters and see real-time updates in the analysis and visualizations.
5. **Report Generation:** Automatically create detailed reports summarizing the analysis findings, including graphs and equations.

### Utilization of SLiCAP Package:
- Use SLiCAP to parse and interpret SPICE netlists, extracting relevant information about circuit topology and components.
- Leverage SLiCAP’s symbolic analysis functions to derive mathematical models of the circuit under different conditions.
- Integrate SLiCAP’s numerical simulation capabilities to validate the symbolic models against actual circuit behavior.
- Employ SLiCAP’s visualization tools to generate insightful graphical representations of the analysis results.
- Implement SLiCAP’s reporting features to document the findings comprehensively.

### Development Steps:
1. **Setup Environment:** Install necessary Python packages including SLiCAP, Matplotlib for plotting, and Tkinter for the GUI.
2. **Netlist Parser:** Develop a parser to read SPICE netlists and convert them into a format SLiCAP can process.
3. **Analysis Module:** Implement functions to perform symbolic and numerical analyses using SLiCAP.
4. **Visualization Module:** Create modules to plot analysis results, allowing users to visualize circuit behavior.
5. **User Interface:** Design a simple yet effective GUI where users can upload netlists, view analysis results, and interact with the circuit parameters.
6. **Report Generator:** Add functionality to automatically generate comprehensive reports summarizing the analysis.
7. **Testing & Validation:** Test the application thoroughly with various SPICE netlists to ensure accuracy and reliability.
8. **Documentation:** Write clear documentation explaining how to use the application and its features.