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🎯 AI
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SpecterOps published findings on LLM-driven EDR evasion, extending Justin Elze's earlier work at TrustedSec on using LLMs for endpoint security product analysis. The core finding: a relatively simple harness using state-of-the-art LLMs can extract detection rules, signatures, and behavioral models from all major EDR products, producing actionable evasion guidance with minimal human intervention.
Background
For years, offensive security researchers have spent evenings reverse-engineering EDR and antivirus engines through manual disassembly and kernel debugging. While effective, this process is slow and becomes a bottleneck when engagements require specific evasions to proceed undetected. LLMs with sufficient capability to drive the reversing effort change the economics of this work dramatically.
What SpecterOps found
Over several months of testing, SpecterOps observed that the "big 5" EDR vendors encountered during assessments are susceptible to LLM-driven reverse engineering and evasion. The harness required to achieve a complete teardown of an EDR's local detections is surprisingly simple. An internal thread was created to collect extracted EDR rules from various vendors, specifically rules designed to stop SpecterOps toolsets.
As EDR after EDR fell to the analysis, automated reports from the testing harness were produced highlighting:
• Mythic agent detection rules
• SCCMHunter behavioral signatures
• LDAP traffic monitoring rules designed to identify Bloodhound collection patterns
• Various other on-host behavioral detection logic
Cortex XDR case study
The post focuses on Palo Alto's Cortex XDR as an example, chosen because "they do some cool things" that made analysis interesting. The author is explicit: every major EDR vendor has been subjected to the same process, and extracted rules, signatures, and models now sit on an internal server.
Implications for detection engineering
The author notes that while red teams have always had private evasion techniques shared quietly, LLMs systematize and accelerate this process. On-host detections that depend on obfuscating their rule logic for effectiveness should be considered compromised. If an LLM can read and reason about detection logic, it can generate targeted evasion guidance at scale.
Disclosure boundaries
The author will not release decryption keys or full rule dumps. The post includes sufficient detail to demonstrate impact without providing turnkey bypass material. Specific LLM models are not named. The harness code is not published. Analysis focuses on on-host detections, not cloud-based or telemetry-driven detections.
For defenders, the operational assumption should be that detection logic is fully visible to motivated adversaries. Detection engineering must shift toward strategies that remain effective even when transparent.
🔹 EDR #LLM #evasion #redteam #CortexXDR
🔗 Source: https://specterops.io/blog/2026/06/29/llm-powered-edr-analysis/#h-what-we-are-seeing