How EDR evasion is changing threat detection

Endpoint Detection and Response (EDR) and Security Information and Event Management (SIEM) have become integral to modern SecOps architecture and threat detection capabilities. However, the urgency of the situation is clear—attackers are deploying increasingly sophisticated techniques to bypass threat detection centered on these systems. The Verizon DBIR 2025 report highlights that in 2024, exploitation of edge devices & VPNs became the fastest-growing breach entry point, jumping from 3% to 22% YoY. This is a call to action for SecOps and modern defenders to adapt to this reality and institute immediate change in threat detection strategy.

How EDR evasion works: attacker tactics

Endpoint agents are not a silver bullet. Not all endpoints in the enterprise can be managed with EDR. Even when installed correctly, EDR can be evaded. EDR evasion includes three primary tactics: blinding, blocking, and hiding. These tactics align with the typical workflow of EDR systems, which involve sensors observing endpoint activity, data being sent to a server, and alerts being generated for malicious activity.

1. Blinding (EDR Tampering): Attackers use various methods to prevent EDR sensors from observing their activities. This tactic involves configuring EDR systems to ignore specific activities or manipulating the operating system features that sensors rely on for monitoring. Examples include:

• Configuring EDR to ignore activity in specific folders.
• Avoiding APIs that EDRs monitor.
• Disabling services or processes associated with EDR agents.
• Installing custom filters to prevent EDR sensors from seeing file activity.

2. Blending into the EDR Environment: Attackers complicate detection efforts by utilizing legitimate credentials and living-off-the-land techniques. Rather than installing malware or commonly recognized hacking tools, they exploit existing tools and resources within the target environment to carry out espionage and other malicious activities, such as data exfiltration.

3. Hiding from EDR Detection: In this tactic, attackers leverage exploit vulnerabilities in connected devices where EDRs can’t be deployed and use those devices as the launching pad for their attacks.

CISA red team flags overreliance on EDR

The Cybersecurity and Infrastructure Security Agency (CISA) conducted a red team assessment (RTA) at the request of a critical infrastructure organization, and the top lesson learned was that the assessed organization had insufficient technical controls to prevent and detect malicious activity. The organization relied too heavily on host-based endpoint detection and response (EDR) solutions and did not implement sufficient network layer protections. The organization’s EDR solutions largely failed to protect it. EDR detected only a few of the red team’s payloads in the organization’s Windows and Linux environments. When the EDR protected the organization from the initial phishing payload, it generated an alert. Unfortunately, network defenders neither read nor responded to it. The red team excelled in bypassing EDR solutions by avoiding basic “known-bad” detections that the tools would capture.

Impact of EDR evasion on threat detection

The reality of EDR evasive attacks has significantly impacted threat detection efforts that rely heavily on EDR and SIEM systems. Detection engineering and SecOps teams can no longer detect the attacks that hide, blend, or tamper with EDR. This is compounded by the limitations of SIEM systems that predominantly depend on logs and alerts generated by EDR solutions. Exploiting vulnerabilities in edge devices and VPNs, emerging as the top breach vector, underscores the critical need for incorporating network data into threat detection strategies, offering a broader and more resilient layer of visibility. By analyzing network traffic, organizations can detect anomalies and patterns that might indicate malicious activity, even when endpoints are compromised or evaded.

Detection engineers can no longer rely on a single methodology—like IOCs, signatures, or machine learning (ML) based detection—to identify threats in their environments. Over-reliance on a singular method results in alert overload, and SecOps teams struggle to prioritize threats in environments overwhelmed by noise and high false positive rates. Instead, the new norm should be a multi-layered detection strategy that collects and analyzes rich contextual data and fuses machine learning, behavioral analytics, curated signatures, and threat intelligence.

Finally, threat hunting should be essential to a comprehensive threat detection strategy. Unlike reactive security measures that rely on alerts generated by EDR and SIEM systems, threat hunting involves actively searching for signs of compromise across the entire environment, including those not discovered by endpoint agents. Threat hunters can identify stealthy attacks that evade detection by employing hypothesis-driven investigations and leveraging diverse data sources, including network telemetry. This proactive approach not only enhances an organization’s ability to detect and respond to threats in real-time but also informs and improves the overall security posture by uncovering vulnerabilities and gaps in existing defenses. Integrating threat hunting into the detection strategy is becoming imperative as attackers evolve their tactics.

Building resilience against EDR evasion with Corelight Open NDR

To build resilience against EDR evasion, organizations can adopt Corelight's multi-layered detection that transcends reliance on endpoint-centric solutions. By integrating machine learning, behavioral analytics, curated signatures, and threat intelligence, Corelight Open NDR delivers prioritized, aggregated alerts based on risk. This holistic approach reduces alert fatigue and enhances detection accuracy, ensuring that SecOps teams can efficiently prioritize and respond to genuine threats. Corelight's ability to provide broad network visibility complements EDR solutions by identifying threats that evade endpoint defenses, such as lateral movement and anomalous activities, and consequently closes critical visibility gaps.

Corelight's high-fidelity security data fuels proactive threat hunting, enabling security teams to detect novel and previously unknown attacks. By enriching detections with deep context and leveraging AI-driven automations, Corelight Open NDR provides evidence-backed summaries and guided triage, streamlining investigations with analyst-ready workflows. This empowers threat hunters to conduct hypothesis-driven investigations and uncover stealthy attacks that evade traditional detection methods. By accelerating the investigative process and providing comprehensive insights, Corelight enhances an organization's overall security posture, equipping defenders with the tools needed to stay ahead of evolving threats and build a resilient defense strategy.

Conclusion

The evolving landscape of cybersecurity threats demands a shift in how organizations approach threat detection. While Endpoint Detection and Response (EDR) and Security Information and Event Management (SIEM) systems remain integral to modern SecOps architecture, their limitations in the face of sophisticated evasion techniques have become increasingly apparent. To counter this SecOps teams must adopt a multi-layered detection strategy that integrates network data, machine learning, behavioral analytics, curated signatures, and threat intelligence.

Corelight Open NDR offers broad network visibility and enriched detections with deep context. Corelight empowers security teams to detect threats that bypass endpoint defenses and leverage all available context to drive faster investigations.