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RevEng.AI is a binary intelligence platform trusted by security operations, threat research, and product security teams to analyze millions of unknown executables - without source code, without sandboxing delays, without compromise.

Median end to end analysis

Malware family attribution precision

Faster than manual reverse engineering

Binaries analysed in the global corpus

Built by reverse engineers. Trusted by Tier 1 SOCs.

Legacy tools analyze one binary at a time. Your adversary ships thousands. RevEng.AI was built ground-up to understand compiled software at the speed and scale of the actual threat - no source code required.

One binary intelligence fabric. Four production-grade capabilities.

From a single uploaded artifact to a fleet-wide attestation pipeline - RevEng.AI gives reverse engineers, threat hunters, and product security teams the same answers, at the same depth, in seconds.

Autonomous threat investigation at machine speed

Find functionally similar code across any binary

Human-readable code from compiled executables

YARA rules generated automatically

Fits into the stack you already operate

Official SDKs, integrations and community plugins make it easy to integrate into existing workflows

Where binary intelligence is mission-critical.

More use cases for our foundational AI models

Vulnerability research

Discover exploitable weaknesses in your own compiled software.

Third-party software assurance

Verify vendor software behaves as expected, without access to source.

Supply chain management​

Continuous assurance across multi-tier suppliers.

Binary checks built into your development pipelines.​

Modernisation & integration​

Recover and integrate legacy binaries.​

Component firmware analysis​

Assure device firmware on individual components.

The latest research from our team

Our team are experts in their fields. Read our research.

Security teams depend on binary analysis for tasks such as incident response, threat intelligence, and auditing third-party software. Two commonly used capabilities accelerate almost all of this work: identifying known components, and understanding how those components are used. This is usually achieved by matching common library functions and turning raw disassembly into readable pseudocode. Function matching has traditionally relied on byte-level and signature-based techniques, including hash

The security landscape of software is evolving at a pace never seen before. Today, AI now writes a significant part of code being created by developers and its abilities are accelerating at a pace no one could have predicted. Few have considered the security impact from AI writing complex code bases in which humans are no longer writing and verifying the code generated. This change in how software is being developed and released is now presenting a number of unsolved security problems for the

Executive Summary This analysis represents the second instalment in a comprehensive examination of the KorPlug malware family. reporting detailed the initial loading vector utilising DLL side-loading techniques against legitimate utilities to achieve code execution. The second-stage payload executes via a designated entry point function. Static analysis of the binary reveals that the Initialise function, invoked by the preceding loader stage, exhibits an anomalous Control Flow Graph (

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