How can identity teams reduce exposure to redirect-based phishing without relying on blocklists?

Why This Matters for Security Teams

Redirect-based phishing is hard to suppress with blocklists because the risky part often appears only after a user, browser, or identity provider has already followed a chain of benign-looking transitions. Attackers rotate domains, redirectors, and error-page logic faster than static indicators can be curated, which makes “known bad” controls fragile. NHI Management Group’s 52 NHI Breaches Analysis shows how quickly real attacks move through identity-dependent paths once trust has been established, and that pattern is consistent with modern phishing infrastructure. Guidance from the Anthropic report on AI-orchestrated cyber espionage also reinforces that adversaries are increasingly using automation to vary infrastructure and evade fixed detections. For identity teams, the practical problem is not just URL filtering; it is detecting the transition pattern that signals credential capture or session hijack before the campaign mutates. In practice, many security teams encounter this only after users have already landed on the final lure, rather than through intentional browser-context monitoring.

Identity teams therefore need a control that reasons over behavior, not reputation. The useful signal is often an abnormal post-error transition, an unexpected redirect after a failed login, or a browser context that no longer matches the normal identity provider flow. That kind of detection is more durable than a blocklist because it survives domain churn and redirector reuse.

NHIMG’s Ultimate Guide to NHIs — Why NHI Security Matters Now is relevant here because the same identity discipline used for service accounts applies to browser-linked attack paths: understand what should happen, then flag what does not. This is also where teams should distinguish between a single suspicious event and a pattern worth operationalising. A one-off redirect may be noise; repeated error-to-credential prompts across multiple users or tenants is a stronger indicator of active phishing infrastructure.

The strongest programs validate detections against internal telemetry before they become standing hunts. That keeps the queue focused on signals the environment can actually confirm, such as IdP logs, browser telemetry, and token issuance events.

How It Works in Practice

The operational model is straightforward: define the expected browser and identity-provider journey, then alert when the journey deviates in a way that matches phishing tradecraft. This usually starts with telemetry from the browser, proxy, or secure web gateway, combined with identity logs from the IdP. The goal is to identify transitions that should be rare, such as:

• login failure followed by an immediate redirect to a lookalike destination
• unexpected domain hops inside a short time window
• prompting for credentials after an error page that normally would not ask again
• session creation without the usual sequence of user interactions

That approach aligns with current guidance from NIST’s AI Risk Management Framework in one important way: controls should be continuously evaluated against real context rather than treated as static allow rules. For identity teams, the equivalent is policy-driven detection. The team defines what “normal” looks like for authentication flows, then uses those baselines to validate new events. Suspicious patterns can be promoted into hunts only after they are corroborated by internal signals such as impossible redirect chains, new user agents, or correlated login anomalies.

NGI-style blocklists still have a role, but they are a late-stage filter, not a primary defense. Better practice is to pair them with browser-context detections and response logic that can quarantine a session, force reauthentication, or add step-up verification when the transition pattern looks wrong. The NHI-centric lesson from the Guide to the Secret Sprawl Challenge is that attackers usually exploit weak visibility first, then persistence second. Redirect-based phishing works the same way: it abuses short-lived trust before defenders can classify the path as malicious. These controls tend to break down in heavily proxied environments with aggressive link rewriting because the original redirect chain is obscured and the browser telemetry loses fidelity.

Common Variations and Edge Cases

Tighter browser-context detection often increases tuning overhead, requiring organisations to balance better phishing resilience against false positives and triage load. That tradeoff is real, especially where remote work, managed browsers, and SSO brokers create noisy transition paths. Current guidance suggests using tiered confidence levels rather than one hard rule: high-confidence redirect anomalies can trigger immediate containment, while lower-confidence cases feed hunt queues for analyst review.

One common edge case is legitimate multi-step authentication that looks suspicious at first glance. Some applications use external IdPs, device posture checks, or chained redirects that are normal for that environment. Another is phishing delivered through compromised legitimate domains, where the redirect itself is not obviously malicious until the destination content loads. In those cases, identity telemetry matters more than URL reputation.

For that reason, teams should validate detections against local baselines and known-good workflows before treating them as policy. The Ultimate Guide to NHIs and Top 10 NHI Issues are useful reminders that identity controls fail most often when teams assume one-size-fits-all enforcement will work everywhere. In this case, it will not. Browser telemetry can be incomplete, redirect chains can be masked by privacy features, and some mobile or embedded-app flows produce patterns that are indistinguishable from phishing without additional context. The safest path is to treat blocklists as a supplement and behavior-based detections as the durable control.

Related resources from NHI Mgmt Group

• How should security teams reduce phishing success without relying on user vigilance alone?
• How should security teams reduce phishing risk without relying only on awareness training?
• How can fraud and identity teams reduce automation risk without relying on static puzzles?
• How should teams reduce the risk of exposed AI credentials being abused?