Introduction
Blocklists were already losing ground before AI entered the picture. Phishing domains have been getting shorter-lived for years, campaigns have been burning infrastructure faster, and the gap between blocklists and attacker campaigns keeps getting wider. AI has finished the job, making blocklists structurally obsolete.
The Problem with Blocklists
Attackers are using AI to generate phishing pages from screenshots in minutes, spin up and tear down infrastructure faster than any blocklist can track, and iterate on tooling at a cadence that makes indicator-based detection functionally useless. 89% of phishing domains are now active for fewer than two days, with just 6.5% surviving past 15 days.
By the time a domain makes it onto a blocklist, the campaign has moved on and the infrastructure has been replaced. If your primary defense against attacks delivered via malicious webpages like AiTM phishing, device code phishing, ClickFix, file downloads, malvertising, and more relies on matching known-bad indicators, you’re always two steps behind.
Disposable by Design
Modern attacks are designed to be disposable from the outset. Attackers aren't waiting to get caught and then pivoting. They're proactively tearing down pages and spinning up new ones to stay ahead of detection, treating each piece of infrastructure as single-use by default.
Attackers are also making that infrastructure harder to analyze while it's live, combining trusted hosting platforms with bot protection, screening checks, and complex redirect chains on top to filter out researchers and automated scanners. 95% of in-browser attacks that Push Security detects use some form of bot protection, often layered with referrer checks and browser fingerprinting.
The Role of AI in Phishing
AI has also collapsed the cost of creating the pages themselves. Attackers were already proficient at cloning pages, but they can now vibe-code entire phishing sites from a screenshot of a legitimate login page — a convincing frontend with a completely unique codebase that looks nothing like the real page it’s based on, and nothing for static analysis to fingerprint.
Phishing delivery increasingly abuses legitimate services — AI chatbot sharing features, search ad placement, in-app messages, and app-generated emails — to inherit the domain reputation of platforms no blocklist would ever flag.
The Erosion of the Tools Layer
The middle of David Bianco's Pyramid of Pain offered a more durable detection surface. Instead of blocking individual domains, you could fingerprint phishing kits — their JavaScript structure, HTML patterns, code signatures — and write detections that survived across dozens or hundreds of campaigns even as infrastructure rotated. That layer is eroding.
The phishing kit ecosystem now fragments through forking, AI-assisted development, and open-source-style code sharing faster than anyone can track. Device code phishing is the clearest example. From early adoption in Russia-linked campaigns in 2024, it’s taken until 2026 to really take off — at which point adoption has exploded from zero criminal kits in the wild to 25+ distinct kits (and counting).
Technique-Level Detection
Genuinely new attack techniques still require human creativity to discover — an attacker has to identify a gap in how a legitimate protocol or feature can be subverted and operationalize it. That kind of innovation hasn't been automated, and detections built around how those techniques work can survive infrastructure rotation, tool proliferation, and kit fragmentation.
Take adversary-in-the-middle (AiTM) phishing. Every AiTM kit — Tycoon, Sneaky 2FA, Evilginx, or countless forks and derivatives (we’re tracking 75+ kits in the wild) implements fundamentally the same interception technique: proxy the victim's session through attacker-controlled infrastructure, relay credentials and MFA tokens in real time, and capture the authenticated session.
Detecting at the technique level targets the thing that's hardest for attackers to change — the mechanics of the attack itself. But it demands two things that most detection programs lack. First, you need visibility where these attacks actually execute. AiTM interception, ClickFix clipboard manipulation, OAuth consent abuse, device code phishing — these techniques play out inside browser sessions where network proxies see encrypted traffic and EDR sees nothing at all.
Conclusion
The industry spent decades building bigger blocklists. AI made that approach structurally obsolete — not just slow, but architecturally incapable of keeping pace. What remains durable is the top of the Pyramid: technique-level behavioral detection, built around how attacks work rather than the infrastructure or tooling that implements them.
Defending at that level requires browser-session visibility and a research pipeline fast enough to stay ahead of the compressing timeline from technique discovery to criminal adoption. At Push Security, we've built that pipeline using AI agents as a force multiplier for human researchers, continuously hunting across browser telemetry and shipping technique-level detections that survive indicator rotation.
Source: BleepingComputer