Enterprise AI Analysis
Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs
This research introduces SYNTHIFY, a novel falsification framework designed to enhance the safety and reliability of AI-enabled control systems. It addresses key limitations of existing methods by synthesizing computationally efficient proxy programs and employing an e-greedy strategy for comprehensive sub-specification coverage. SYNTHIFY significantly improves success rates, reduces falsification time, and diversifies violation detection, making AI control system testing more practical and effective for enterprise applications.
Executive Impact: Transforming AI System Falsification
83.5%
Higher Falsification Success Rate
12.8x
Faster Violation Detection
137.7%
More Diverse Sub-Specification Coverage
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The core innovation of SYNTHIFY lies in its two-phase falsification process. First, it synthesizes a computationally efficient proxy program to mimic the AI controller's functionality, significantly reducing execution time. Second, it employs an e-greedy strategy to intelligently sample promising sub-specifications from complex conjunctive safety specifications, ensuring comprehensive coverage and diverse violation detection. This dual approach tackles the scalability and comprehensiveness challenges inherent in testing AI-enabled control systems.
SYNTHIFY utilizes a sketch-based program synthesis driven by an Evolution Strategy (ES) algorithm to generate linear controllers as proxies. These proxies are far more efficient than the original Deep Neural Network (DNN) AI controllers. During falsification, the e-greedy strategy balances exploring new sub-specifications and exploiting known vulnerable ones, guiding a Simulated Annealing (SA)-based algorithm. Detected violations are verified against the original AI controller; spurious ones trigger a refinement process for the proxy program.
Evaluated on eight publicly available control systems, SYNTHIFY demonstrated an 83.5% higher falsification success rate and was 12.8 times faster at finding a single safety violation compared to PSY-TALIRO, a state-of-the-art tool. It also achieved 137.7% more diverse sub-specification coverage. These results highlight SYNTHIFY's superior effectiveness and efficiency in identifying safety violations in AI-enabled control systems, even with large AI controllers.
83.5%
Increased Falsification Success Rate Over Baseline
SYNTHIFY Falsification Workflow
AI Controller & Safety Spec
→
Synthesize Proxy Program
→
Sample Sub-Specification
→
Falsification (Proxy)
→
Check Spurious/Real
→
Refine Proxy (if spurious)
→
Report Real Violation
| Feature | SYNTHIFY Advantage | PSY-TALIRO (Baseline) |
|---|---|---|
| Scalability for AI Models | Synthesized proxy programs for efficient execution, reducing AI controller runtime bottleneck. | Direct execution of computationally expensive AI models, leading to scalability issues. |
| Sub-Specification Coverage | e-greedy strategy for balanced exploration/exploitation, achieving 137.7% more diverse coverage. | Tends to over-exploit easily violated sub-specifications, resulting in incomplete coverage. |
| Overall Efficiency | 12.8x faster at finding a single violation, 5.6x faster for 50 trials. | Slower due to high AI model execution costs and less efficient search strategy for conjunctive specs. |
| Refinement Process | Spurious violations used to refine proxy programs, improving fidelity. | No explicit proxy refinement mechanism. |
Impact on Self-Driving Systems
In a self-driving car scenario, SYNTHIFY successfully found 7.8x more safety violations within the same time budget, ensuring the car's heading angle remained within 90° and distance to centerline below 2.0m. The proxy program (δ = 0.20706786 * η - 0.31286586 * d - 0.27174068) efficiently mimicked the AI controller, demonstrating its practical value for safety-critical AI-enabled control systems. This indicates SYNTHIFY's robust performance even for systems with complex conjunctive specifications.
7.8x More
Violations Found
Significantly Higher
Proxy Program Efficiency
Advanced ROI Calculator: Quantify Your AI Safety Savings
Estimate the potential cost savings and reclaimed engineering hours by implementing SYNTHIFY for your AI-enabled control system testing.
Your Enterprise AI Safety Implementation Roadmap
A structured approach to integrate SYNTHIFY into your development and testing workflows.
Phase 1: Discovery & Integration
Our experts conduct a deep dive into your existing AI-enabled control systems and safety specifications. We then seamlessly integrate SYNTHIFY into your current testing infrastructure.
Phase 2: Proxy Program Synthesis & Refinement
We work with your teams to synthesize and continuously refine proxy programs that accurately mimic your AI controllers, ensuring high fidelity and computational efficiency.
Phase 3: Automated Falsification & Coverage Expansion
SYNTHIFY's e-greedy strategy automatically identifies diverse safety violations, providing your engineers with actionable insights and comprehensive sub-specification coverage.
Phase 4: Continuous Monitoring & Reporting
Establish ongoing falsification runs with detailed reporting and analytics, maintaining high safety standards for your evolving AI systems.
Ready to Enhance Your AI System's Safety?
Don't let hidden safety violations compromise your AI-enabled control systems. Partner with us to leverage SYNTHIFY's advanced falsification capabilities for unparalleled reliability and efficiency.
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