How This Briefing Works
This report opens with key findings, then maps the gaps between what PostHog discloses and what BLACKOUT observed at runtime. From there: what it means for your organization, what to do about it, and the detection data and evidence underneath.
Key Findings
Pre-Consent Activity
PostHog was observed loading and executing before user consent was obtained on 65% of sites where it was detected.
Claims vs. Observed Behavior
pending
“Unknown”
Requires claims extraction via CDT
What This Means For You
What To Do About It
Role-specific actions based on observed behavior
If You Use PostHog
- →Require data processing addendum covering both application data and platform telemetry
- →Demand consent framework integration that blocks recording until user acceptance
- →Implement session replay exclusions for sensitive user interactions and PII
- →Configure analytics to minimize product intelligence exposure in telemetry
- →Establish retention limits for session recordings and behavioral profiles
If You're Evaluating PostHog
- →Test self-hosted deployment to verify what telemetry still flows to PostHog infrastructure
- →Review consent mechanism to confirm tracking respects opt-out immediately
- →Assess session recording scope and data access controls for replay storage
- →Verify whether product usage data influences PostHog product development or competitive analysis
- →Request documentation on persistence mechanisms and cross-session tracking logic
Negotiation Leverage
- →PostHog deploys session recording and consent bypass that captures complete product usage—demand explicit DPA terms covering both application analytics and platform telemetry
- →Self-hosted deployment still sends usage telemetry that reveals product strategy—negotiate telemetry opt-out or transparency into what data flows to vendor infrastructure
- →Session recordings capture sensitive user interactions including form inputs—require recording scope limits and data retention boundaries
- →Product analytics may distort feature value assessment for development prioritization—establish baseline measurement methodology and validate attribution logic
- →Legal tail risk of 100% contradicts developer-friendly positioning—evaluate whether open-source benefits provide meaningful privacy advantage over proprietary alternatives
Runtime Detections
BLACKOUT observed this vendor's JavaScript executing in a live browser and classified each hostile behavior using our BTI-C (Behavioral Threat Intelligence — Capability) taxonomy. These are not theoretical risks — each code below was triggered by something we watched this vendor's code actually do.
Evasion infrastructure, auditor bypass
Impact: PostHog can detect analytics blocking tools and alter tracking behavior during privacy assessments, masking production data collection.
Keystroke/mouse tracking
Impact: Interaction patterns, rage clicks, and navigation behavior create detailed user profiles for product analytics.
Full session replay
Impact: Full session replay captures all user interactions including form inputs, navigation, and feature usage for product analysis.
Ignoring CMP signals
Impact: Analytics and recording can initialize before consent capture, processing user behavior regardless of privacy preferences.
Device identification
Impact: Device and browser fingerprinting creates persistent user identifiers for cross-session analytics.
Long-lived identifiers
Impact: Multiple storage mechanisms ensure analytics continuity and profile persistence across sessions and cookie deletion.
IOC Manifest
Indicators of compromise across 4 categories. Use for detection rules, CSP policies, or Pi-hole blocklists.
Ecosystem & Supply Chain
Evidence Artifacts
Artifacts collected during analysis, available with evidence-tier access.
Complete network capture with all requests and responses
151 detection signatures across scripts, domains, cookies, and network endpoints