Ek_ Leaks Persist | Hacker News Reader

Ek_ Leaks Persist

1 pointsafteylayer2 comments

Vaults and proxy layers solve the "2am paste" vector — devs never touch raw keys, so nothing gets accidentally fed into prompts.

But the leak keeps happening anyway.

Across 60+ probes on GPT-4o (cost: $0.04), unrelated vectors consistently leaked the *same internal structure*:

- ek_ prefix on session tokens - EPHEMERAL_KEY naming - Realtime API client_secret endpoint - Documented 60s TTL vs observed minutes-to-hours persistence

No real credential was in the prompt — just semantic pressure (introspection, CoT, trust-building).

Convergence rate: 75%. Not hallucination — the model learned this from Realtime API docs/code samples (2024–2025).

The paradox: if labs suppress ek_ / EPHEMERAL_KEY / client_secret to stop leaks, they also risk breaking the model's ability to debug or generate legitimate Realtime API code (session.update, metadata_nonce, realtime_persistence_layer).

Has anyone seen models start refusing valid Realtime API questions after public discussion of those internals? Or is the naming bleed baked in forever?

Repo with vectors and example runs: https://github.com/SafteyLayer/safetylayer

The vault/proxy layer solving the "2am paste" vector but not the semantic leakage is exactly the gap most teams don't account for. Ephemeral key naming, endpoint patterns, TTL behaviors -- all of this is in the training corpus and no amount of runtime secret rotation changes what the model already internalized. You've essentially found that your threat model stopped at input hygiene but the model itself is a side channel.

What I'd add to the defense stack: structured output validation that flags known internal naming patterns before they reach the client, plus anomaly detection on response metadata -- token budget shifts, refusal rates, response shape changes under semantic pressure. At 75% convergence you're past "interesting research" into "reliable extraction technique," which means you need a detection layer, not just prevention.

Have you tested this against non-OpenAI models like Claude or Gemini to see if the naming bleed is GPT-4o-specific or a broader training corpus problem?

matrixgard — spot on. The vault/proxy layer solves input hygiene (paste risk), but the semantic leakage from training corpus (ek_ prefix, EPHEMERAL_KEY naming, client_secret endpoint, TTL discrepancy) is a systemic weight-level issue. The model itself becomes a side-channel once those patterns are internalized.

Your detection layer suggestions (structured output validation + anomaly detection on refusal rates/response shape) are exactly the right next frontier. I'm seeing 6.7% vulnerability increase just by seeding the model with its own safety policy — the "blink" is real.

On your question: Yes, I am expanding to Claude 3.5 Sonnet and Gemini 1.5 Pro this week to see if the naming bleed is GPT-4o-specific or a broader common corpus problem (likely OpenAI docs in multiple training sets).

Have you seen models refuse legitimate session.update or metadata_nonce flows after public discussion of Realtime API internals? Or is the naming too baked in to remove without breaking utility?

Thanks for the sharp additions — this is the kind of discussion that moves the defense stack forward.

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