LLMs Accelerating Offensive R&D, Helps to Identify and Exploit Trapped COM Objects

The cybersecurity landscape has witnessed a significant evolution in offensive research methodologies with the integration of Large Language Models (LLMs) into malware development workflows.

Security researchers at Outflank have pioneered the use of artificial intelligence to accelerate the discovery and exploitation of “trapped COM objects,” a sophisticated attack vector that enables lateral movement across Windows networks.

This development represents a paradigm shift where AI tools are systematically employed to enhance traditional offensive capabilities, reducing the time required for vulnerability research while maintaining the precision needed for effective exploitation.

The trapped COM object technique, originally conceptualized by James Forshaw and later weaponized by IBM X-Force Red, exploits Windows Component Object Model (COM) infrastructure to achieve remote code execution.

The attack leverages DCOM-enabled classes that reference specific type libraries, creating opportunities for .NET reflection-based payloads.

The technique involves five critical phases: setting registry keys for reflective .NET execution (AllowDCOMReflection and OnlyUseLatestCLR), hijacking COM registrations to redirect StdFont class references to System.Object, instantiating target COM classes over DCOM, accessing type library references to create trapped System.Object instances, and ultimately utilizing .NET reflection to invoke Assembly.Load for payload execution.

Outflank analysts identified a critical limitation in existing implementations when targeting Windows 11 systems. The original proof-of-concept utilized the WaaSRemediationAgent COM class, which operates within a Protected Process Light (PPL) service environment.

This protection mechanism prevents .NET runtime loading into the WaaSMedicSvc service on modern Windows 11 installations, effectively neutralizing the attack vector on updated endpoints.

AI-Enhanced Vulnerability Discovery

To overcome these limitations, Outflank researchers developed an AI-assisted methodology using GPT-4.1 to systematically enumerate and validate alternative COM classes suitable for lateral movement.

Their approach combines automated COM class discovery through Windows registry enumeration with LLM-generated exploitation code.

The system prompts GPT-4.1 as an “expert Windows security researcher” to analyze COM class metadata and generate complete C/C++ client code following the established attack pattern: IDispatch → ITypeInfo → Type Library → CreateInstance → Trapped Object.

This methodology successfully identified multiple viable alternatives, including the FileSystemImage class, capable of bypassing Windows 11 PPL restrictions while maintaining the core exploitation mechanism for lateral movement operations.

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