The Q1 vulnerability pulse

Q1 2026 vulnerability data reveals persistent risks from legacy CVEs, with networking gear comprising 20% of Known Exploited Vulnerabilities. AI-related CVEs increased, signaling growing risks from AI-enabled attacks capable of exploiting zero-days. Cisco Talos highlighted the abuse of the n8n automation platform for phishing and malware delivery, bypassing traditional security via trusted infrastructure. Specific incidents include an actively exploited Adobe zero-day and a fake Claude website distributing PlugX RAT. Additionally, Russian hackers were attributed to a destructive attack on a Swedish thermal plant. Defenders must prioritize patch management, implement behavioral detection for automation platform traffic, and restrict endpoint communications. AI-driven email security is recommended to analyze semantic intent. The overall threat landscape shows no relief in exploitation rates, emphasizing the need for visibility into environment assets and proactive mitigation of decade-old vulnerabilities to prevent compromise.

Thor provides an overview of the Q1 2026 vulnerability statistics, highlighting key trends in legacy CVEs and the evolving impact of AI on the threat landscape.

Q1 2026 vulnerability data reveals persistent risks from legacy CVEs, with networking gear comprising 20% of Known Exploited Vulnerabilities. AI-related CVEs increased, signaling growing risks from AI-enabled attacks capable of exploiting zero-days. Cisco Talos highlighted the abuse of the n8n automation platform for phishing and malware delivery, bypassing traditional security via trusted infrastructure. Specific incidents include an actively exploited Adobe zero-day and a fake Claude website distributing PlugX RAT. Additionally, Russian hackers were attributed to a destructive attack on a Swedish thermal plant. Defenders must prioritize patch management, implement behavioral detection for automation platform traffic, and restrict endpoint communications. AI-driven email security is recommended to analyze semantic intent. The overall threat landscape shows no relief in exploitation rates, emphasizing the need for visibility into environment assets and proactive mitigation of decade-old vulnerabilities to prevent compromise. Thor provides an overview of the Q1 2026 vulnerability statistics, highlighting key trends in legacy CVEs and the evolving impact of AI on the threat landscape. Welcome to this week’s edition of the Threat Source newsletter. The first quarter of 2026 passed faster than a misconfigured firewall rule gets exploited — and the last few weeks have been firmly stamped with the "software supply chain compromise" label, with headlines surrounding incidents involving Trivy , Checkmark , LiteLLM , telnyx and axios . This edition stays focused on vulnerability statistics, although you can view Dave and Nick's Talos blogs for more information about these incidents. Known Exploited Vulnerabilities (KEVs) stayed roughly in line with 2025 numbers — no dramatic spike, but no room for relief either. What does stand out? Networking gear accounted for 20% of KEV-related vulnerabilities, and that number is expected to climb as the year progresses. If the trend from 2025 holds, this won't be the high-water mark. Patch management remains one of the industry's most persistent challenges, and I understand all the operational complexity that comes with it. That said, it still stings to come across CVEs with disclosure dates reaching back to 2009 — and roughly 25% of the CVEs we're tracking date to 2024 or earlier. Old vulnerabilities don't retire. They wait. It starts with visibility: Knowing what's actually running in your environment is the prerequisite for everything else. Overall CVE counts increased in Q1, with March showing the sharpest climb. Whether that reflects improved disclosure pipelines, increased researcher activity, ora genuine uptick in vulnerability density, the trend line from 2025 hasn't flattened — if anything, it's still pointing up. Using the keyword methodology described here , 121 CVEs with AI relevance were identified in Q1 — more than Q1 2025, though consistent with what adoption trends would predict. As AI components become more deeply embedded across the software stack, this number will keep climbing. Given the recent developments with models like the Mythos preview and the industry teaming up in initiatives like Project Glasswing , I'm curious how the trajectory will change moving forward. If you haven't read about it: “During our testing, we found that Mythos Preview is capable of identifying and then exploiting zero-day vulnerabilities in every major operating system and every major web browser when directed by a user to do so.” - Anthropic Frontier Red Team That's a substantial capability jump in agentic coding and reasoning, which eventually needs to be implemented early in the development lifecycle. And as Anthony points out, those capabilities will become available to adversaries. Read Cisco's guidance on defending in the age of AI-enabled attacks for more. Will we see fewer CVEs or even more negative times-to-exploit (TTEs)? It's on us. Defenders need to get ahead of the adversaries, and at the same time, we need to pay attention to (sometimes decade-old) vulnerabilities. The one big thing Cisco Talos has identified a significant increase in the abuse of n8n, an AI workflow automation platform, to facilitate malicious campaigns including malware delivery and device fingerprinting. Attackers are weaponizing the platform’s URL-exposed webhooks to create phishing lures that bypass traditional security filters by leveraging trusted, legitimate infrastructure. By masking malicious payloads as standard data streams, these campaigns effectively turn productivity tools into delivery vehicles for remote access trojans and other cyber threats. Why do I care? The abuse of legitimate automation platforms exploits the inherent trust organizations place in these tools, which often neutralizes traditional perimeter-based security defenses. Because these platforms are designed for flexibility and seamless integration, they allow attackers to dynamically tailor payloads and evade detection...