Six months after launch, an audit turned up patient ID numbers sitting inside a third-party dashboard nobody remembered turning on. No one typed those IDs in on purpose; they just rode along in a log payload nobody thought to check.
That’s the exact failure a Reddit user posting as Born-Abalone3246 has spent months chasing down while auditing agentic pipelines for healthcare and finance teams. The author’s core finding: most leaks have nothing to do with the model itself. They happen in the plumbing around it: tool calls, retrieval, logging, memory, subagent handoffs. That’s the stuff that passes a demo clean and shows up in a compliance review six months later.
Quick version, before we get into it: this post walks through 12 leak patterns the author documented, plus why each one matters. Then it covers deterministic fixes, not “ask another LLM to check”, worth building into your own pipeline.
Why This Actually Matters 🚨
A demo only tests the happy path. Compliance audits test everything else. The SQL query your agent quietly sent counts. So does the log line Datadog captured by default. So does the subagent handoff that skipped your sanitization filter because nobody wired it in twice.
For teams in healthcare, finance, or anywhere HIPAA or SOC 2 applies, one of these leaks isn’t a bug ticket. It’s a breach notification, a client conversation nobody wants to have, or a fine. That’s why this Redditor’s list is worth reading closely. It’s a map of exactly where leaks tend to hide, before an auditor finds them for you.
How to Spot (and Gate) Every Leak 🛠️
The author lists these roughly in the order the team ran into them. Worth working through in order, since later ones build on the earlier failure modes:
- Prompt-only redaction. Telling the model “don’t output SSNs or patient IDs” in the system prompt works fine in a demo. It falls apart fast under adversarial input, multi-hop tool chains, or context compaction.
- Tool argument leakage. The final answer looks clean, but the SQL query or shell command running behind the scenes often carries raw, unredacted PII straight through.
- RAG retrieval chunk bleed. Vector stores pull in neighboring chunks that have nothing to do with the question but happen to contain someone else’s data.
- Persistent session memory. Agents that carry memory across sessions can quietly write sensitive identifiers into logs or state stores that never get cleaned up.
- Log sink over-export. Tools like Datadog or Sentry capture the full payload string by default, so sensitive data lands in a third-party dashboard nobody approved.
- Subagent handoff leakage. A coordinator hands a task summary to a subagent, and the sanitization filter that worked on the main path gets skipped on that internal message.
- Unaudited proxy traffic. Most teams have no idea what metadata rides along on every outbound request. The author built an open-source tool, tigerless-cost-xray, to inspect local proxy traffic per turn if you want to check your own setup.
- Probabilistic filtering that isn’t reliable. Using a second LLM to check whether the first one leaked something is slow, expensive, and not trustworthy enough to bet compliance on. The fix: phi-boundary-gate, a tool the author built for rule-based pattern matching. It redacts across prompts, RAG flows, tool arguments, and memory logs before anything reaches an external provider.
- Cached prompt prefix snooping. In multi-tenant setups, shared prefix caching can theoretically expose one tenant’s prompt templates to another if isolation isn’t airtight.
- Error message back-propagation. A database exception dumps the raw query, customer emails and medical record numbers included, straight into the agent’s prompt buffer.
- Embeddings inversion. Vectors get treated as safe to share externally, but they can often be inverted back into the original text.
- Unsanitized browsing tools. An agent’s web tool scrapes an internal wiki page and re-sends architecture details to a public LLM endpoint, with nobody meaning for that to happen.
Tips & Tricks Worth Stealing ✅
- Treat tool arguments as a separate leak surface from the final output. One commenter on the thread called this one “terrifying because nobody thinks to look there until it’s way too late,” and that’s the right instinct.
- Skip the “LLM judges LLM” approach for anything compliance-sensitive. Deterministic, rule-based redaction at the network boundary is slower to build and much easier to trust. Worth the extra afternoon.
- Check subagent handoffs separately from your main path. A filter that works once doesn’t mean it’s wired into every internal message.
- Treat database and API error messages as untrusted input. They love to dump raw data straight into your prompt buffer.
- If you’re piping requests through a shared proxy, actually look at what’s riding along on each call before you assume it’s clean.
Go Audit Your Own Pipeline 👀
Go check whichever step above sounds closest to your own stack. Chances are one of these twelve is already happening quietly in your logs.
Head over to the original thread on r/PromptEngineering if you want the full back-and-forth, including how other builders are handling their own network boundary. Worth the read before your next audit finds it for you.
12 data leaks and compliance risks in agentic LLM pipelines (and deterministic ways to gate them)
by u/Born-Abalone3246 in PromptEngineering