OpenAnt: LLM-Powered Vulnerability Discovery Through Code Decomposition, Adversarial Verification, and Dynamic Testing
arXiv:2606.19149v1 Announce Type: cross Abstract: Automated vulnerability discovery in large codebases remains challenging: traditional static analysis produces high false-positive rates, while dynamic approaches such as fuzzing require substantial infrastructure and often target narrow classes of bugs. Recent advances in large language models (LLMs) enable semantic reasoning about program behavior, but applying LLMs to repository-scale security analysis introduces challenges related to context management, cost, and verification. We present OpenAnt, an open-source vulnerability discovery syste
The proliferation of complex codebases and the increasing sophistication of AI models like LLMs make now an opportune moment for applying AI to automate vulnerability discovery.
Automated, efficient, and accurate vulnerability discovery reduces cyber risk, enhances software security, and can significantly impact the speed and cost of software development and deployment across all sectors.
The development of LLM-powered tools like OpenAnt shifts vulnerability discovery towards more automated, semantically-aware approaches, potentially reducing false positives and expanding the scope of detectable bugs compared to traditional methods.
- · Cybersecurity industry
- · Software developers
- · Cloud providers
- · Organizations with large codebases
- · Attackers relying on undiscovered vulnerabilities
- · Traditional static analysis tool vendors
- · Manual security auditing services
Openant facilitates the faster identification and patching of software vulnerabilities, enhancing overall system security.
Improved security tooling leads to more robust software products, diminishing the economic viability of certain cyberattack vectors.
The widespread adoption of AI-driven security tools could spark an AI-powered arms race in cybersecurity between defenders and attackers, necessitating continuous innovation.
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Read at arXiv cs.LG