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    What EvilTokens teaches us about defending against AI-assisted attacks

    Cybercriminals don’t need AI to compromise an organization, but AI is making its way into classic attack methods and changing how quickly such attacks can progress. Tasks that once required attackers to manually sift through information and piece together how an organization operates can be accelerated by AI, reducing the time defenders have to detect and intervene. A recent example of this is EvilTokens, a phishing-as-a-service platform that helps attackers gain access to Microsoft 365 inboxes and then uses AI to analyze the environment to determine the most effective next steps. First identified in February 2026, EvilTokens exploits the device authorization grant flow of OAuth 2.0 to gain access to Microsoft accounts without having to create fraudulent login pages. After taking down Storm-2992, the threat actor believed to be behind the scheme, in September, Microsoft said the EvilTokens operation leveraged AI “at every step of the attack chain.” As AI removes more of the friction that slows attackers down, security will depend not only on how quickly teams respond to threats, but on how much an attacker can accomplish before they do. AI is removing friction from the attack lifecycle Most cyberattacks can be broadly viewed as progressing through three stages: access, expansion and impact. First, an attacker gains access to the environment, whether by stealing credentials, exploiting a vulnerability or socially engineering an employee. From there, they expand what they can access and establish persistence. Finally, they use their position to achieve an objective such as stealing intelligence, committing fraud, deploying ransomware or disrupting operations. Across the attack lifecycle, AI can give attackers greater speed, scale, sophistication and stamina, what we at SimSpace call “The Four S’s.” EvilTokens demonstrates how those advantages can be packaged in a single service. It allows attackers to exploit Microsoft’s legitimate device code authentication flow, convincing victims to authorize attacker access without handing over their passwords. Once inside, AI identifies who controls payments, which business relationships carry trust and which invoices or transactions present opportunities for fraud. It then recommends impersonation targets and drafts convincing fraudulent messages grounded in real business conversations. EvilTokens shows what happens when AI-assisted analysis and decision-making become a packaged capability that cybercriminals can purchase. The attacker still needs to gain an initial foothold, but AI can remove friction from all the work that comes after. Performance must be evaluated differently Mean-time-to-detect (MTTD) or mean-time-to-respond (MTTR) are metrics widely used by security teams to measure their performance. AI-assisted attacks change the relevance of those measurements because the attacker’s timeline can accelerate as well. If an AI-assisted attacker compromises an employee’s account and it takes 30 minutes for security teams to detect it, the attacker may be able to accomplish far more during that window than previously possible. And unless detection triggers immediate containment, the attacker’s clock keeps running and the scope of the potential damage increases. This changes the context in which MTTD and MTTR should be evaluated. A detection that occurs before an attacker has moved beyond the original account is much more valuable than one that occurs after persistence has been established and the attacker has identified the people and processes they intend to exploit. Speed must be measured by how quickly a threat can reach impact, not just an organization’s previous performance. Controls should be tested together Another challenge facing security teams is that security technologies are often assessed individually, despite the fact that real attacks move across multiple systems. An EvilTokens-style attack might interact with many enterprise systems, such as email security, identity verification or network monitoring tools, and any one of those layers could potentially interrupt the attack. Testing those defenses independently can show whether they protect individual functions, but it doesn’t provide organizations with much certainty about what happens when an attacker moves across them. Understanding how the defenses perform together requires testing the complete attack path, rather than individual techniques in isolation. Security teams can recreate realistic attack scenarios and measure when the initial activity is detected, what the attacker accomplishes before and after detection, which controls interrupt the attack and how long it takes to contain the activity. Running those scenarios repeatedly as threats and technologies evolve can also show whether the window between initial access and defensive intervention is shrinking or growing. More importantly, it gives security teams an opportunity to identify where an attack can progress and address those weaknesses before they’re exploited in a real incident. This is the idea behind preemptive security, an emerging practice in which security teams focus on identifying and addressing the plausible actions an attacker might take before they actually do, rather than detecting and responding to active threats. Security has to keep pace with the adversary EvilTokens is an early example of how malicious actors can apply AI to established cybercriminal techniques . Anything that allows criminals to move faster, manage more targets and operate with less expertise is likely to be adopted and repackaged. As that happens, security teams will need to reconsider what constitutes “good” defensive performance. A historically impressive MTTD or MTTR means less when an AI-assisted attacker can accomplish significantly more within that same window. Defenders can’t control how quickly attackers adopt these capabilities, but they can influence how much progress an attacker can make before they’re discovered and stopped. Increasingly, that will require looking beyond how quickly organizations respond to attacks and asking where an attacker is likely to make progress before an incident occurs. The more organizations can identify and secure those vulnerabilities in advance, the less they have to rely on winning a race against an AI-assisted attacker once an attack has already begun.

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    Source: https://www.csoonline.com/article/4239703/what-eviltokens-teaches-us-about-defending-against-ai-assisted-attacks.html

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