Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy Programs Article Swipe
Jieke Shi
,
Zhou Yang
,
Junda He
,
Bowen Xu
,
Dongsun Kim
,
Deqiang Han
,
David Lo
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.04986
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2410.04986
Given the increasing adoption of modern AI-enabled control systems, ensuring their safety and reliability has become a critical task in software testing. One prevalent approach to testing control systems is falsification, which aims to find an input signal that causes the control system to violate a formal safety specification using optimization algorithms. However, applying falsification to AI-enabled control systems poses two significant challenges: (1)~it requires the system to execute numerous candidate test inputs, which can be time-consuming, particularly for systems with AI models that have many parameters, and (2)~multiple safety requirements are typically defined as a conjunctive specification, which is difficult for existing falsification approaches to comprehensively cover. This paper introduces Synthify, a falsification framework tailored for AI-enabled control systems. Our approach performs falsification in a two-phase process. At the start, Synthify synthesizes a program that implements one or a few linear controllers to serve as a proxy for the AI controller. This proxy program mimics the AI controller's functionality but is computationally more efficient. Then, Synthify employs the $ε$-greedy strategy to sample a promising sub-specification from the conjunctive safety specification. It then uses a Simulated Annealing-based falsification algorithm to find violations of the sampled sub-specification for the control system. To evaluate Synthify, we compare it to PSY-TaLiRo, a state-of-the-art and industrial-strength falsification tool, on 8 publicly available control systems. On average, Synthify achieves a 83.5% higher success rate in falsification compared to PSY-TaLiRo with the same budget of falsification trials. The safety violations found by Synthify are also more diverse than those found by PSY-TaLiRo, covering 137.7% more sub-specifications.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2410.04986
- https://arxiv.org/pdf/2410.04986
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4403964476Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2410.04986Digital Object Identifier
- Title
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Finding Safety Violations of AI-Enabled Control Systems through the Lens of Synthesized Proxy ProgramsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-10-07Full publication date if available
- Authors
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Jieke Shi, Zhou Yang, Junda He, Bowen Xu, Dongsun Kim, Deqiang Han, David LoList of authors in order
- Landing page
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https://arxiv.org/abs/2410.04986Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2410.04986Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2410.04986Direct OA link when available
- Concepts
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Proxy (statistics), Lens (geology), Control (management), Computer science, Computer security, Business, Psychology, Engineering, Artificial intelligence, Machine learning, Petroleum engineeringTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.numerous | 69 |
| abstract_inverted_index.performs | 122 |
| abstract_inverted_index.process. | 127 |
| abstract_inverted_index.publicly | 216 |
| abstract_inverted_index.requires | 64 |
| abstract_inverted_index.software | 20 |
| abstract_inverted_index.strategy | 170 |
| abstract_inverted_index.systems, | 8 |
| abstract_inverted_index.systems. | 119, 219 |
| abstract_inverted_index.tailored | 115 |
| abstract_inverted_index.testing. | 21 |
| abstract_inverted_index.Simulated | 185 |
| abstract_inverted_index.Synthify, | 111, 202 |
| abstract_inverted_index.algorithm | 188 |
| abstract_inverted_index.available | 217 |
| abstract_inverted_index.candidate | 70 |
| abstract_inverted_index.difficult | 100 |
| abstract_inverted_index.framework | 114 |
| abstract_inverted_index.prevalent | 23 |
| abstract_inverted_index.promising | 174 |
| abstract_inverted_index.two-phase | 126 |
| abstract_inverted_index.typically | 92 |
| abstract_inverted_index.AI-enabled | 6, 56, 117 |
| abstract_inverted_index.PSY-TaLiRo | 233 |
| abstract_inverted_index.approaches | 104 |
| abstract_inverted_index.efficient. | 164 |
| abstract_inverted_index.implements | 136 |
| abstract_inverted_index.increasing | 2 |
| abstract_inverted_index.introduces | 110 |
| abstract_inverted_index.violations | 191, 243 |
| abstract_inverted_index.$ε$-greedy | 169 |
| abstract_inverted_index.PSY-TaLiRo, | 207, 255 |
| abstract_inverted_index.algorithms. | 51 |
| abstract_inverted_index.challenges: | 62 |
| abstract_inverted_index.conjunctive | 96, 178 |
| abstract_inverted_index.controller. | 151 |
| abstract_inverted_index.controllers | 142 |
| abstract_inverted_index.parameters, | 86 |
| abstract_inverted_index.reliability | 13 |
| abstract_inverted_index.significant | 61 |
| abstract_inverted_index.synthesizes | 132 |
| abstract_inverted_index.(2)~multiple | 88 |
| abstract_inverted_index.controller's | 158 |
| abstract_inverted_index.optimization | 50 |
| abstract_inverted_index.particularly | 77 |
| abstract_inverted_index.requirements | 90 |
| abstract_inverted_index.falsification | 54, 103, 113, 123, 187, 212, 230, 239 |
| abstract_inverted_index.functionality | 159 |
| abstract_inverted_index.specification | 48 |
| abstract_inverted_index.falsification, | 30 |
| abstract_inverted_index.specification, | 97 |
| abstract_inverted_index.specification. | 180 |
| abstract_inverted_index.Annealing-based | 186 |
| abstract_inverted_index.comprehensively | 106 |
| abstract_inverted_index.computationally | 162 |
| abstract_inverted_index.time-consuming, | 76 |
| abstract_inverted_index.state-of-the-art | 209 |
| abstract_inverted_index.sub-specification | 175, 195 |
| abstract_inverted_index.industrial-strength | 211 |
| abstract_inverted_index.sub-specifications. | 259 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |