Adversary-in-the-Middle (AITM) attacks represent a critical threat to modern organizations. These attacks allow cybercriminals to intercept communications between users and legitimate services, bypassing multi-factor authentication and gaining unauthorized access to sensitive systems. As remote work and cloud adoption increase, AITM attacks have become a primary attack vector for threat actors targeting credentials and session tokens. According to recent 2025 reports, AITM attacks have increased by 46% year-over-year, with financial services, healthcare, online gaming, and e-commerce emerging as the most frequently targeted sector[1].
AITM attacks position threat actors between users and legitimate services by intercepting network communications or redirecting victims to malicious proxy sites. These attacks capture authentication credentials in real-time, steal session tokens and cookies, bypass MFA protections, and maintain persistent access to compromised accounts.
Traditional security tools struggle to detect AITM attacks effectively because of inherent visibility limitations. Legacy SIEMs rely on logs that reflect legitimate-looking sessions, including successful MFA, allowing attackers to bypass detection entirely. EDR solutions focus on endpoint activity, but since AITM attacks often use stolen tokens without interacting with the endpoint, they see nothing suspicious. NDR tools may catch behavioral anomalies, but when SSL is terminated at the application layer, encrypted proxy traffic obscures visibility. Collectively, these tools are inefficient against AITM tactics because they lack the contextual depth and real-time session awareness needed to identify such sophisticated threats.
Gurucul REVEAL, a unified data and security analytics platform, leverages advanced AI, big data and behavioral analytics to protect against AITM attacks through detection layers aligned with the MITRE ATT&CK framework. The platform monitors authentication patterns, analyzes credential usage behavior, identifies attempts to bypass security controls, and detects communication patterns between compromised accounts and attacker infrastructure.
Gurucul’s detection capabilities identify AITM attacks through behavioral analytics that detect behavior patterns indicative of credential interception and session manipulation. Our platform monitors, using advanced chain modeling, for suspicious authentication, network positioning activities, and post-compromise behaviors that signal ongoing AITM operations, revealing the full scope of attacker behavior and intent for faster, more informed response
AITM attacks create distinct signatures that can be detected through comprehensive monitoring of user activities, network traffic, and authentication patterns. By analyzing these activities in context, security teams can identify active AITM campaigns holistically and respond before attackers achieve their objectives.
The Gurucul platform includes pre-build detection models covering 100% of MITRE Tactics and Techniques and ITDR modules.
Gurucul’s automated, risk-based playbooks, integrated with authentication tools, enable real-time mitigation to stop threats before they spread. A full listing of Smart Content models is available for customers in the Gurucul Documentation Portal.
When potential AITM activity is detected, Gurucul’s Agnetic AI provides contextual information about attack progression, offers MITRE ATT&CK-aligned remediation recommendations, and generates incident reports for security teams.
Organizations using Gurucul’s AITM detection capabilities achieve faster threat detection through real-time identification of AITM indicators, comprehensive monitoring across hybrid and multi-cloud environments, automated response through AI-driven playbooks, and reduced investigation time through contextual intelligence.
Ready to strengthen your organization’s defenses against AITM attacks and other credential-based threats? Visit our website to review detailed case studies showcasing real-world attack scenarios and their resolution, access technical whitepapers on detection methodologies, watch a video demo of Gurucul in action, or schedule a live demo to see how Gurucul’s security analytics platform can enhance your threat detection and response capabilities. Don’t wait for an attack to test your defenses — take proactive steps to protect your organization today.
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About the Author:
Adam Burris, Senior Director, Threat Detection & Response
Adam Burris is a Cybersecurity leader with 15+ years of specialized experience in managed security services, threat detection, and SOC operations.
AITM attacks bypass multi-factor authentication and hijack session tokens in real time, giving attackers persistent, unauthorized access to sensitive systems. With the rise of remote work and cloud adoption, these attacks have surged by 46% year-over-year.
Legacy SIEMs, EDR, and NDR tools lack the real-time session awareness and contextual depth needed to detect AITM behavior. These attacks often mimic legitimate activity, making them invisible to tools that rely on logs or endpoint telemetry alone.
Gurucul uses advanced AI, behavioral analytics, and prebuilt detection models aligned with MITRE ATT&CK to identify suspicious authentication patterns, token abuse, network redirection, and lateral movement. Our platform reveals the full attacker lifecycle for fast, informed response.
Gurucul’s Agentic AI provides full context on the attack, suggests MITRE-aligned remediation steps, and triggers automated, risk-based playbooks. This enables immediate containment and faster investigation, reducing dwell time and risk exposure.
