https://github.com/Akileshdash/rl-guided-static-analysis-rust
A reinforcement learning–guided framework for reducing false positives in static memory safety analysis of Rust programs.
https://github.com/Akileshdash/rl-guided-static-analysis-rust
Last synced: 5 days ago
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A reinforcement learning–guided framework for reducing false positives in static memory safety analysis of Rust programs.
- Host: GitHub
- URL: https://github.com/Akileshdash/rl-guided-static-analysis-rust
- Owner: Akileshdash
- Created: 2025-12-31T13:14:30.000Z (6 months ago)
- Default Branch: main
- Last Pushed: 2025-12-31T17:48:16.000Z (6 months ago)
- Last Synced: 2026-01-05T03:34:10.150Z (6 months ago)
- Language: Python
- Size: 2.14 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-Rust-Checker - rl-guided-static-analysis-rust - 01-26 | (Dynamic Checkers)
README
# Mitigating False Positives in Rust Static Analysis via Reinforcement Learning
This repository contains the implementation of our research paper: **"Mitigating False Positives in Static Memory Safety Analysis of Rust Programs via Reinforcement Learning"**.
## Overview
Static analysis tools like Rudra suffer from high false positive rates (50%+), which diminish developer trust and increase manual review effort. This project presents a novel reinforcement learning-based approach that:
- **Reduces false positives** by more than doubling precision from 25.6% to 59.0%
- **Maintains high recall** of 74.6%, identifying nearly three-quarters of true bugs
- **Integrates selective fuzzing** to validate ambiguous warnings dynamically
- **Outperforms LLM baselines** by 17.1 percentage points in F1 score
- **Achieves 65.2% accuracy** with fuzzing vs. 54.5% without
### Key Features
- Proximal Policy Optimization (PPO) agent for warning classification
- ~87 MIR-level semantic features extracted from Rust's Mid-level Intermediate Representation
- Selective dynamic validation via cargo-fuzz integration
- Three-action decision space: classify as true positive, false positive, or invoke fuzzing
- Cost-aware reward function balancing accuracy and computational efficiency
---
## Repository Structure
```
.
├── data/ # Raw dataset
├── RL_output/ # Model predictions
│ ├── rl.json # RL without fuzzing results
│ └── rl_with_fuzzer.json # RL with fuzzing results
├── llm/ # LLM baseline comparisons
│ ├── jsons/ # LLM prediction outputs
│ │ ├── llm_results_claude_opus4_1.json
│ │ ├── llm_results_codellama_34b.json
│ │ ├── llm_results_gpt-4o-mini.json
│ │ ├── llm_results_llama3_70b.json
│ │ ├── llm_results_llama3_8b.json
│ │ └── llm_results_mixtral.json
│ └── scripts/ # LLM evaluation scripts
│ ├── claude_opus_llm_baseline.py
│ ├── gpt-4o-mini_llm_baseline.py
│ ├── run_codellama34b.py
│ ├── run_llama3_70b.py
│ ├── run_llama3_8b.py
│ └── run_mixtral.py
├── rl_without_fuzzing.py # PPO agent WITHOUT fuzzing
├── rl_with_fuzzing.py # PPO agent WITH fuzzing integration
├── ground_truth.json # Manually labeled warnings dataset (1,247 labels)
├── training_data.json # Static analysis warnings with features (4,879 samples)
├── result_table.py # Generate comparison tables and metrics
├── requirements.txt # Python dependencies
└── README.md # This file
```
---
## Quick Start
### Prerequisites
- **Python**: 3.8 or higher
- **Rust toolchain** (for actual fuzzing)
- **cargo-fuzz** (for real fuzzing integration)
### Installation
1. **Clone the repository**
```bash
git clone https://github.com/Akileshdash/rl-guided-static-analysis-rust.git
cd rl-guided-static-analysis-rust
```
2. **Create a virtual environment (recommended)**
```bash
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
```
3. **Install dependencies**
```bash
pip install -r requirements.txt
```
---
## Dataset
### Dataset Construction
Our dataset consists of:
- **4,879 unique warnings** from Rudra's Unsafe Dataflow and Send/Sync Variance checkers
- Collected from **~20,000 crates** from crates.io containing unsafe code
- **1,247 true positives (25.6%)** and **3,632 false positives (74.4%)**
- Manually labeled by domain experts with **82.7% inter-rater agreement** (Cohen's κ = 0.63)
### Dataset Split
- **Training set**: 70% (3,415 warnings)
- **Validation set**: 15% (732 warnings)
- **Test set**: 15% (732 warnings)
### `ground_truth.json`
Contains manually labeled ground truth for each warning.
```json
[
{
"file": "aarc-0.3.2_2.json",
"false_positive": 0
}
]
```
---
## Running the Models
### 1. PPO Agent (Without Fuzzing)
Trains a reinforcement learning agent using only static features extracted from MIR.
```bash
python rl_without_fuzzing.py
```
**Expected Output:**
```
======================================================================
PPO Agent for Rust Static Analysis False Positive Reduction
======================================================================
Loading data...
Loaded 1247 ground truth labels
Loaded 4879 training samples
Dataset split:
Training: 3415 samples (70%)
Validation: 732 samples (15%)
Test: 732 samples (15%)
Starting training for 200 epochs...
Network architecture: 87 -> 256 -> 128 -> 2
----------------------------------------------------------------------
Epoch 1 | Acc: 52.34% | Loss: 12.3456 | Reward: -1234
Epoch 10 | Acc: 54.12% | Loss: 8.7654 | Reward: 456
...
Epoch 200 | Acc: 54.52% | Loss: 2.1234 | Reward: 2345
----------------------------------------------------------------------
Training complete!
Test Set Results:
Accuracy: 54.5%
Precision: 49.6%
Recall: 67.9%
F1 Score: 57.3%
MCC: 0.117
Results saved to RL_output/rl.json
```
**Output File:** `RL_output/rl.json`
- Contains predictions for all test samples
- Training history (accuracy, loss, reward per epoch)
- Metadata with final metrics
---
### 2. PPO Agent with Fuzzing Integration
Trains an agent that can selectively invoke cargo-fuzz for dynamic validation of ambiguous warnings.
```bash
python rl_with_fuzzing.py
```
**Expected Output:**
```
======================================================================
PPO Agent with Fuzzing Integration
Rust Static Analysis False Positive Reduction
======================================================================
Loading data...
Loaded 1247 ground truth labels
Loaded 4879 training samples
Starting training with fuzzing integration for 200 epochs...
Dataset size: 4879 samples
Action space: 0=Classify_FP, 1=Classify_TP, 2=Invoke_Fuzzing
----------------------------------------------------------------------
Epoch 1 | Acc: 53.21% | Loss: 11.2345 | Reward: -1123 | Fuzz: 35.2%
Epoch 10 | Acc: 58.45% | Loss: 7.6543 | Reward: 678 | Fuzz: 28.4%
...
Epoch 200 | Acc: 65.23% | Loss: 1.8765 | Reward: 3456 | Fuzz: 23.1%
----------------------------------------------------------------------
Training complete!
Fuzzing Statistics:
Total invocations: ~23% of warnings
Successful bug findings: 342
Clean executions: 785
Fuzzing success rate: 30.3%
Test Set Results:
Accuracy: 65.2%
Precision: 59.0%
Recall: 74.6%
F1 Score: 65.9%
MCC: 0.323
AUC-ROC: 0.661
AUC-PR: 0.554
Results saved to RL_output/rl_with_fuzzer.json
```
**Output File:** `RL_output/rl_with_fuzzer.json`
- Contains predictions with fuzzing metadata
- Training history including fuzzing rates
- Fuzzing statistics (invocations, success rate)
- Confidence scores for each classification
---
### 3. Generate Results Table
Compare all models (PPO, PPO+Fuzzing, LLM baselines) with standard metrics.
```bash
python result_table.py
```
**Expected Output:**
```
╔════════════════════════╦══════════╦═══════════╦════════╦═════════╦═══════╦═══════╦═══════╗
║ Approach ║ Accuracy ║ Precision ║ Recall ║ F1 ║ MCC ║ ROC ║ PR ║
╠════════════════════════╬══════════╬═══════════╬════════╬═════════╬═══════╬═══════╬═══════╣
║ Raw Rudra Output ║ — ║ 0.256 ║ 1.000 ║ 0.407 ║ — ║ — ║ — ║
║ RL + Fuzzing ║ 0.652 ║ 0.590 ║ 0.746 ║ 0.659 ║ 0.323 ║ 0.661 ║ 0.554 ║
║ RL (No Fuzzing) ║ 0.545 ║ 0.496 ║ 0.679 ║ 0.573 ║ 0.117 ║ 0.557 ║ 0.481 ║
║ Claude Opus 4.1 ║ 0.533 ║ 0.486 ║ 0.669 ║ 0.563 ║ 0.094 ║ 0.546 ║ 0.474 ║
║ ChatGPT-4o Mini ║ 0.490 ║ 0.453 ║ 0.650 ║ 0.534 ║ 0.010 ║ 0.505 ║ 0.452 ║
║ CodeLlama 34B ║ 0.431 ║ 0.409 ║ 0.598 ║ 0.486 ║-0.112 ║ 0.446 ║ 0.425 ║
║ Llama3 8B ║ 0.487 ║ 0.437 ║ 0.487 ║ 0.460 ║-0.026 ║ 0.487 ║ 0.443 ║
║ Mixtral 8x7B ║ 0.326 ║ 0.328 ║ 0.476 ║ 0.388 ║-0.336 ║ 0.339 ║ 0.392 ║
║ Llama3 70B ║ 0.503 ║ 0.360 ║ 0.136 ║ 0.197 ║-0.082 ║ 0.469 ║ 0.437 ║
╚════════════════════════╩══════════╩═══════════╩════════╩═════════╩═══════╩═══════╩═══════╝
Key Findings:
- RL+Fuzzing improves F1 by 17.1 percentage points over best LLM baseline
- Precision more than doubles from 25.6% (raw Rudra) to 59.0%
- Fuzzing provides 10.7 pp accuracy gain and 8.6 pp F1 gain over RL-only
- Selective fuzzing invoked on ~23% of warnings for cost-aware validation
```
---
## Requirements
### Python Packages
```txt
numpy>=1.21.0
torch>=1.9.0
scikit-learn>=0.24.0
```
### For Real Fuzzing
For actual cargo-fuzz integration:
```bash
# Install Rust toolchain
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
# Install cargo-fuzz
cargo install cargo-fuzz
# Install nightly Rust (required for fuzzing)
rustup install nightly
rustup default nightly
```
---
## Configuration
### Hyperparameters
Both `rl_without_fuzzing.py` and `rl_with_fuzzing.py` expose key hyperparameters that can be tuned:
```python
# Network architecture
state_size = 87 # MIR feature dimensions (~87 features)
hidden1 = 256 # First hidden layer
hidden2 = 128 # Second hidden layer
action_size = 2 # Binary classification (3 for fuzzing variant)
# Training parameters (PPO)
epochs = 200 # Training epochs
learning_rate = 0.0003 # Learning rate for PPO
gamma = 0.99 # Discount factor
clip_epsilon = 0.2 # PPO clipping parameter
value_coef = 0.5 # Value function coefficient
entropy_coef = 0.01 # Entropy coefficient for exploration
# Reward function
correct_reward = 15 # Correct classification
incorrect_penalty = -15 # Incorrect classification
fuzzing_cost = -5 # Fuzzing invocation cost
fuzzing_bonus_bug = 10 # Fuzzing found bug (confirms TP)
fuzzing_bonus_clean = 8 # Fuzzing ran clean (suggests FP)
fuzzing_bonus_helpful = 3 # Fuzzing helpful but not definitive
```
### Feature Extraction
The feature extraction pipeline processes MIR-level information organized into three categories:
#### 1. MIR-Level Semantic Features (~50 features)
- **Type system properties**: Generic parameters, trait bounds, generic nesting depth
- **Ownership and borrowing**: Borrow ratios, nesting depth, smart pointer usage
- **Control-flow**: Cyclomatic complexity, loop nesting, panic paths
- **Unsafe operations**: Lifetime bypass category, distance to dangerous operations
#### 2. Structural Code Features (~20 features)
- **Package-level signals**: Download counts, unsafe code prevalence
- **API surface**: Public vs. private API warnings
- **Code metrics**: Lines of code, parameter counts, comment density
#### 3. Analysis-Specific Features (~17 features)
- **Rudra metadata**: Checker type, precision level
- **Clustering**: Multiple warnings at nearby locations
- **Pattern type**: Panic safety, higher-order invariants, Send/Sync variance
**Total: ~87 normalized features** after correlation removal and domain filtering
---
## Methodology
### Reinforcement Learning Formulation
Our approach formulates false positive reduction as a Markov Decision Process (MDP):
- **State Space**: 87-dimensional feature vectors extracted from MIR
- **Action Space**:
- Action 0: Classify as False Positive
- Action 1: Classify as True Positive
- Action 2: Invoke Dynamic Fuzzing (fuzzing variant only)
- **Reward Function**: Balances classification accuracy against computational cost
- **Policy Network**: Two hidden layers (256 → 128 units) with ReLU activations and dropout
- **Algorithm**: Proximal Policy Optimization (PPO)
### Selective Fuzzing Strategy
The agent learns to invoke fuzzing based on confidence estimates:
1. **Q-value gap**: Smaller gaps indicate uncertainty
2. **Policy entropy**: Higher entropy reflects lower confidence
3. **Cost-aware decision**: Balances fuzzing cost (-5) against expected information gain
Fuzzing outcomes are encoded into state representation:
- **Crash/sanitizer violation** → Strong TP evidence (+10 bonus)
- **Clean execution** → Moderate FP evidence (+8 bonus)
- **Timeout/inconclusive** → Weak signal (+3 bonus)
---
## Bug Pattern Coverage
Rudra targets three critical classes of memory safety bugs in unsafe Rust:
1. **Panic Safety**: Stack unwinding during panics can violate invariants
- Example: CVE-2020-36317 in `String::retain()`
2. **Higher-Order Invariants**: Semantic assumptions about generic functions
- Example: CVE-2020-36323 in `join()` (impure `Borrow` implementations)
3. **Send/Sync Variance**: Missing or incorrect thread-safety bounds
- Example: CVE-2020-35905 in futures-rs `MappedMutexGuard`
---
## Performance Comparison
### Key Improvements Over Baselines
| Metric | Raw Rudra | RL Only | RL+Fuzzing | Improvement |
|--------|-----------|---------|------------|-------------|
| Precision | 25.6% | 49.6% | **59.0%** | +130% |
| Recall | 100% | 67.9% | **74.6%** | -25.4% (acceptable) |
| F1 Score | 40.7% | 57.3% | **65.9%** | +61.9% |
| Accuracy | — | 54.5% | **65.2%** | +19.6% |
### Fuzzing Impact
- **10.7 percentage points** accuracy gain over RL-only
- **8.6 percentage points** F1 score improvement
- **Invoked on ~23%** of warnings (cost-effective)
- **30.3% success rate** in finding bugs when invoked
---
## Acknowledgments
- Original Rudra implementation by Yechan Bae et al.
- Rust compiler team for MIR infrastructure
- cargo-fuzz developers for fuzzing integration