threat-modeling.md

Threat Model Analysis for huggingface/candle

Threat: Malicious Model Loading

• Description: Loading a crafted model file from an untrusted source using candle. This malicious model could exploit vulnerabilities within candle's model loading or inference engine to execute arbitrary code on the server, potentially leading to full system compromise or data exfiltration. The attacker leverages candle's model loading functionality to introduce malicious code.
• Impact: Critical. Full system compromise, data breach, denial of service, reputational damage.
• Affected Candle Component: Model Loading Module (functions related to loading model weights from files, formats like safetensors, ggml, etc.).
• Risk Severity: Critical
• Mitigation Strategies:
  • Model Source Validation: Strictly load models only from trusted and verified sources.
  • Model Integrity Checks: Implement checksum or digital signature verification for model files before loading using candle.
  • Sandboxing: Execute candle inference within a sandboxed environment (containers, VMs) to limit the impact of potential exploits originating from malicious models loaded by candle.
  • Input Sanitization (Model Paths): If model paths are user-provided to candle's loading functions, sanitize and validate them to prevent path traversal attacks.

Threat: Model Deserialization Vulnerabilities

• Description: Exploiting vulnerabilities in candle's model deserialization process. A maliciously crafted model file, when loaded by candle, could trigger vulnerabilities in the deserialization code, leading to arbitrary code execution, denial of service, or memory corruption within the candle process. The attacker directly targets candle's parsing of model files.
• Impact: Critical. Arbitrary code execution, denial of service, memory corruption, potential data breach.
• Affected Candle Component: Model Loading Module, specifically deserialization functions for different model formats (e.g., safetensors deserialization, ggml deserialization within candle).
• Risk Severity: Critical
• Mitigation Strategies:
  • Keep Candle Updated: Regularly update candle to the latest version to ensure you have the latest security patches for deserialization vulnerabilities.
  • Dependency Updates (Relevant to Deserialization): Ensure dependencies used by candle for deserialization are also up-to-date.
  • Fuzzing and Security Audits (External): Rely on the security practices of the candle development team, which should include fuzzing and security audits of the deserialization code within candle.

Threat: Input Injection leading to Resource Exhaustion (Candle Inference Engine)

• Description: Sending crafted input data to the application that, when processed by candle's inference engine, triggers excessive resource consumption (CPU, memory). This can lead to denial of service specifically due to how candle handles certain inputs during inference. The attacker exploits the computational nature of candle's inference process with specific inputs.
• Impact: High. Denial of service, performance degradation, resource exhaustion, service unavailability directly caused by overloading candle's inference engine.
• Affected Candle Component: Inference Engine (core inference functions, model execution within candle).
• Risk Severity: High
• Mitigation Strategies:
  • Input Validation and Sanitization: Thoroughly validate and sanitize input data before it is processed by candle. Define and enforce expected input formats, sizes, and ranges relevant to the model and candle's processing.
  • Resource Limits (Candle Process): Implement resource limits (CPU time, memory limits) specifically for the candle inference processes to prevent resource exhaustion attacks targeting candle.
  • Timeout Mechanisms (Inference Requests): Set timeouts for inference requests processed by candle to prevent long-running, resource-intensive requests from causing denial of service.
  • Rate Limiting (Inference API): Implement rate limiting on the API endpoints that trigger candle inference to control the volume of requests and prevent overwhelming candle.

Threat: Denial of Service due to Algorithmic Complexity Exploitation (Candle/Model Specific)

• Description: Exploiting specific input patterns that trigger worst-case algorithmic complexity within the model itself when executed by candle. By sending these inputs, an attacker can cause candle's inference process to become extremely slow and resource-intensive, leading to denial of service. This is directly related to the interaction between the model's algorithm and candle's execution of it.
• Impact: High. Denial of service, performance degradation, service unavailability due to computationally expensive inference within candle.
• Affected Candle Component: Inference Engine, specific model architectures and algorithms as executed by candle.
• Risk Severity: High
• Mitigation Strategies:
  • Algorithmic Complexity Analysis (Model and Candle Interaction): Analyze the algorithmic complexity of the models when run with candle. Identify input patterns that could lead to performance bottlenecks in candle's execution of the model.
  • Input Size and Complexity Limits (Model Specific): Impose limits on input size and complexity that are relevant to the model's algorithmic complexity when processed by candle (e.g., sequence length for transformer models in candle).
  • Timeout Mechanisms (Inference Requests): Implement timeouts for inference requests handled by candle.
  • Load Balancing and Scalability (Candle Inference): Distribute the candle inference workload across multiple instances to mitigate the impact of resource exhaustion on a single instance running candle.

Threat: Unsafe Code Vulnerabilities in Candle

• Description: Bugs within unsafe Rust code blocks directly within candle's codebase could lead to memory safety vulnerabilities (buffer overflows, use-after-free, etc.). Exploiting these vulnerabilities could allow attackers to achieve arbitrary code execution or denial of service specifically by targeting flaws in candle's internal unsafe code.
• Impact: High to Critical. Arbitrary code execution, denial of service, memory corruption, potential data breach directly resulting from vulnerabilities in candle's code.
• Affected Candle Component: Any module or function within candle that utilizes unsafe code blocks.
• Risk Severity: High to Critical
• Mitigation Strategies:
  • Code Audits (External - Candle Project): Rely on security audits and code reviews conducted by the candle development team and the Rust community, specifically focusing on unsafe code within candle.
  • Static Analysis Tools (Candle Project): Encourage and rely on the use of static analysis tools by the candle developers to detect potential issues in candle's code, including unsafe code vulnerabilities.
  • Community Security Practices (Candle/Rust): Benefit from the broader Rust and Hugging Face communities' security practices and vulnerability reporting mechanisms for candle. Keep candle updated to incorporate fixes for any identified unsafe code vulnerabilities.