threat-modeling.md

Threat Model Analysis for tencent/ncnn

Threat: Malicious Model Files

Description: An attacker provides a crafted .param or .bin model file designed to exploit vulnerabilities in ncnn's model parsing logic. Upon loading, ncnn attempts to parse the malicious model, triggering the vulnerability. * Impact: * Denial of Service (DoS): Application crashes or hangs. * Remote Code Execution (RCE): Exploitation of parsing vulnerabilities allows arbitrary code execution. * Information Disclosure: Leakage of sensitive data due to unexpected behavior. * Affected ncnn Component: Model Loader (parsing logic for .param and .bin files). * Risk Severity: Critical * Mitigation Strategies: * Model Source Validation: Only load models from trusted and verified sources. * Checksum/Signature Verification: Verify model file integrity before loading. * Sandboxing: Run ncnn in a sandboxed environment. * Regular Updates: Keep ncnn library updated.

Threat: Malicious Input Data for Inference

Description: An attacker provides carefully crafted input data to trigger vulnerabilities during ncnn inference. This could exploit edge cases or bugs in ncnn's processing layers. * Impact: * Denial of Service (DoS): ncnn crashes or hangs due to unexpected input. * Unexpected Application Behavior: Incorrect inference results or application malfunctions. * Potentially Remote Code Execution: If input can trigger memory corruption in inference engine. * Affected ncnn Component: Inference Engine (layers and operators processing input data). * Risk Severity: High * Mitigation Strategies: * Input Validation (Data): Thoroughly validate and sanitize input data before inference. * Error Handling: Implement robust error handling around ncnn inference calls. * Resource Limits: Set resource limits for ncnn inference. * Fuzzing: Consider fuzzing ncnn with varied input data.

Threat: Memory Corruption Vulnerabilities

Description: ncnn (C++ codebase) is susceptible to memory safety issues (buffer overflows, use-after-free, etc.). Exploitation can be triggered by malicious models or input data. * Impact: * Remote Code Execution (RCE): Arbitrary code execution with application privileges. * Denial of Service (DoS): Application crashes and instability. * Information Disclosure: Potential leakage of sensitive data from memory. * Affected ncnn Component: Core ncnn library code (memory management, data processing functions). * Risk Severity: Critical * Mitigation Strategies: * Regular ncnn Updates: Keep ncnn updated for security patches. * Static/Dynamic Analysis: Use analysis tools to identify memory safety issues. * Memory Sanitizers (Development/Testing): Use sanitizers to detect memory errors early. * Operating System Security Features: Leverage ASLR and DEP.

Threat: Compromised ncnn Library/Build Artifacts

Description: Malicious code injected into ncnn library source or pre-built binaries if obtained from untrusted sources or build process is compromised. * Impact: * Remote Code Execution (RCE): Malicious code execution on systems using compromised library. * Complete Application Compromise: Full control over applications using compromised ncnn. * Data Exfiltration: Stealing sensitive data processed by the application. * Affected ncnn Component: Entire ncnn library. * Risk Severity: Critical * Mitigation Strategies: * Official Sources: Download ncnn from official and trusted sources only. * Build Process Security: Secure build environment and process. * Checksum/Signature Verification (Binaries): Verify integrity of downloaded binaries. * Supply Chain Security: Implement supply chain security best practices. * Build from Source (if feasible): Build ncnn from source in a controlled environment.