RevEng.AI raises $15M to secure AI-generated software

2026-05-27 · Source: Tech.eu - Tech.eu · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy, Software Development & Engineering · Depth: Fundamental Awareness, quick

Summary

RevEng.AI, a cybersecurity company, has secured \$15 million in Series A funding to expand its binary-level software verification platform. This funding round was led by NATO Innovation Fund, with participation from Sands Capital, In-Q-Tel, IQ Capital, and Episode One. The platform addresses the increasing risk of software supply chain attacks and the challenges of securing AI-generated code by analyzing compiled software directly at the binary level. Its foundational AI model, BinNet, identifies hidden vulnerabilities, backdoors, suspicious functionality, and abnormal changes in executables, firmware, and third-party applications before deployment. Unlike traditional tools focused on source code, RevEng.AI operates without requiring source code access, making it critical for verifying closed-source and third-party software. The company is experiencing early demand from enterprise and defence customers, and the investment will support its growth and deployment.

Key takeaway

For Cybersecurity Procurement Managers evaluating third-party or AI-generated software, you must prioritize binary-level verification. Traditional source code reviews are insufficient for today's complex supply chains and closed-source components. Your teams should integrate platforms like RevEng.AI to automatically detect hidden vulnerabilities and malicious functionality in executables before deployment. This strengthens your organization's resilience against supply chain attacks and is essential for securing critical infrastructure.

Key insights

RevEng.AI's BinNet uses binary-level AI analysis to secure software supply chains against hidden threats in compiled, AI-generated, and third-party code.

Principles

• Binary-level verification is crucial for untrustworthy software.
• AI-generated code necessitates automated security checks.
• Software supply chain security requires independent verification.

Method

RevEng.AI's BinNet AI model analyzes compiled software binaries to identify vulnerabilities, backdoors, and abnormal changes before deployment, without source code access.

In practice

• Identify hidden components in third-party software.
• Detect malicious behavior in closed-source executables.
• Verify software releases against trusted versions.

Topics

• RevEng.AI
• Software Supply Chain Security
• Binary Analysis
• AI-Generated Code
• Cybersecurity Funding
• Vulnerability Detection

Best for: CTO, VP of Engineering/Data, Director of AI/ML, Investor, Tech Journalist, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Tech.eu - Tech.eu.