Autonomous Intelligent Agents for Natural-Language-Driven Web Execution with Integrated Security Assurance
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866916013761626112 |
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| author | Pasupuleti, Vinil Bayyavarapu, Siva Rama Krishna Varma Tyagi, Shrey |
| author_facet | Pasupuleti, Vinil Bayyavarapu, Siva Rama Krishna Varma Tyagi, Shrey |
| contents | Modern web test suites rot. A UI refactor breaks locators, a timing change causes race conditions, and within weeks developers abandon the suite entirely. This paper presents an AI-driven autonomous testing framework that addresses these failure modes through five integrated strategies - navigation reliability, context-aware selector generation, post-generation validation, smart wait injection, and failure learning - implemented over a containerised worker architecture that decouples orchestration from long-running browser execution. Evaluated across four production applications and 176 scenarios, the framework improves script generation success from 55% to 93%, achieves an 8x reduction in navigation failures, eliminates 80% of timing-related race conditions, and reduces test creation time by 75% compared to manual Selenium authoring. The framework extends naturally to security validation: testers describe attack scenarios in plain English - "try accessing another user's invoice" - which the agent converts to OWASP Top 10-aligned browser probes, detecting 85% of authentication bypass vulnerabilities and 95% of input validation flaws with false positive rates below 12%. Natural-language-driven security testing of this kind represents, to our knowledge, a novel contribution to the field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_15281 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Autonomous Intelligent Agents for Natural-Language-Driven Web Execution with Integrated Security Assurance Pasupuleti, Vinil Bayyavarapu, Siva Rama Krishna Varma Tyagi, Shrey Cryptography and Security Artificial Intelligence Modern web test suites rot. A UI refactor breaks locators, a timing change causes race conditions, and within weeks developers abandon the suite entirely. This paper presents an AI-driven autonomous testing framework that addresses these failure modes through five integrated strategies - navigation reliability, context-aware selector generation, post-generation validation, smart wait injection, and failure learning - implemented over a containerised worker architecture that decouples orchestration from long-running browser execution. Evaluated across four production applications and 176 scenarios, the framework improves script generation success from 55% to 93%, achieves an 8x reduction in navigation failures, eliminates 80% of timing-related race conditions, and reduces test creation time by 75% compared to manual Selenium authoring. The framework extends naturally to security validation: testers describe attack scenarios in plain English - "try accessing another user's invoice" - which the agent converts to OWASP Top 10-aligned browser probes, detecting 85% of authentication bypass vulnerabilities and 95% of input validation flaws with false positive rates below 12%. Natural-language-driven security testing of this kind represents, to our knowledge, a novel contribution to the field. |
| title | Autonomous Intelligent Agents for Natural-Language-Driven Web Execution with Integrated Security Assurance |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2605.15281 |