How is secrets detection different from PII detection?

Both can run in one API call. PII (emails, phones) typically yields a suspicious verdict. Secrets yield an unsafe verdict and should usually be blocked rather than redacted and forwarded.

Where should secrets scanning run?

At minimum: chat input handlers, RAG ingestion, tool output bridges, and optionally model output renderers. Always enforce server-side in the path that builds the provider payload.

Does detection rotate compromised keys automatically?

No. Detection flags exposure; your incident response runbook must revoke and rotate credentials, scope provider logs, and notify stakeholders per policy.

Secrets Detection for LLM Applications

Q: What is secrets detection for LLM applications?

It is scanning text bound for LLM context—prompts, RAG chunks, tool outputs—for credential-shaped strings such as API keys, private keys, bearer tokens, and password-like key-value pairs before logging or provider transmission.

Secrets detection for LLM applications is the practice of scanning text—user prompts, retrieved documents, tool outputs, and model responses—for credential-shaped strings such as API keys, private keys, bearer tokens, and password-like key-value pairs before that text is logged, stored in a vector index, or transmitted to a model provider. Because LLM pipelines treat text as opaque context, a single pasted .env line or PEM block can propagate through chat history, RAG retrieval, and observability backends unless you intercept it at a defined trust boundary.

What counts as a secret in LLM context

In production scanners, "secrets" typically include:

Cloud and SaaS API keys — AWS, Stripe, Google, Slack, OpenAI-style prefixes
Private keys — PEM-encoded RSA, EC, or OpenSSH blocks
Bearer and session tokens — JWT-like strings in Authorization headers
Credential pairs — password=, api_key=, token= assignments in config snippets
OAuth and PAT formats — GitHub, Slack, and similar high-entropy tokens

These differ from general PII (emails, phone numbers) in severity: a detected secret usually warrants unsafe verdict semantics and hard blocking rather than redaction-only handling. Background on PII categories: What Is PII Detection?.

Why LLM apps are high-risk for secret exposure

Traditional web apps keep secrets server-side. LLM apps encourage users and developers to paste:

Entire configuration files for "debugging help"
curl commands copied from runbooks
Stack traces with injected headers
Internal wiki pages indexed into RAG without sanitization

Model providers may store prompts per their data policies. Your logs may capture request bodies at INFO level. Annotation platforms used for RLHF may export conversations. Each hop multiplies exposure.

Read LLM Data Leakage for the full map of leakage paths and How to Prevent API Keys from Leaking into AI Prompts for key-specific workflows.

IdenticAPI secrets detection

The PII & Secrets Detection API scans for both PII and secrets in one call—useful because real prompts often mix them.

Endpoint: POST /api/v1/security/pii-secrets

{
  "text": "Deploy with AWS key AKIA0000000000000000 and notify user@example.com",
  "redact": true
}

Response excerpt:

{
  "verdict": "unsafe",
  "risk": "high",
  "findings": [
    {
      "category": "aws_access_key",
      "reason": "Detected aws access key (secret)",
      "confidence": 0.95
    },
    {
      "category": "email",
      "reason": "Detected email (pii)",
      "confidence": 0.95
    }
  ],
  "redacted_text": "Deploy with AWS key [API_KEY] and notify [EMAIL]"
}

Secrets elevate the verdict to unsafe even when PII is present. Documentation: PII & Secrets Detection docs. Manual testing: PII Checker.

Integration points in LLM architectures

Location	What to scan	Action on `unsafe`
Chat input handler	User message + attachments (extracted text)	Block + user guidance
RAG ingestion	Each document chunk	Quarantine chunk or reject document
Tool output bridge	JSON/text returned to model	Strip or block
Log pipeline	Structured log message fields	Redact or drop event
Output renderer	Model completion before UI	Block display, alert

Place scanning server-side in the request path that constructs the provider payload. Client-only checks are bypassable.

Policy design

Define explicit rules per channel:

Public customer chatbot

Block all secrets (unsafe)
Redact PII (suspicious + redact: true)
Never forward blocked content to the model for "helpful debugging"

Internal engineering assistant

Block production secret patterns
Allow synthetic test keys only if clearly labeled (still risky—prefer blocking all sk_ patterns)
Route ambiguous findings to security review instead of the model

Agent with code execution

Block secrets before code reaches a sandbox
Sandbox should not have outbound network access to exfiltrate discovered keys

Document policies in your PII and Secrets Leakage Checklist reviews.

Synthetic test fixtures

Build regression tests with fictional credentials:

sk-test_abcdefghijklmnopqrstuvwxyz123456
ghp_abcdefghijklmnopqrstuvwxyz1234567890AB
AKIA0000000000000000
xoxb-000000000000-0000000000000-SyntheticSlackToken
api_key=sk-test_notrealvalue1234567890

Expected: verdict: "unsafe", non-empty findings, appropriate category per pattern.

For private key blocks, see How to Detect Private Keys and Tokens in Text.

Pairing secrets detection with other guards

Secrets scanning addresses data exfiltration. Complementary controls:

Prompt injection detection — Stops instruction manipulation in untrusted text
Agent action guard — Validates tool calls before execution
Unified guard — Orchestrates multiple checks in one request

Secrets detection does not replace vault storage, rotation, or least-privilege IAM—but it catches the common mistake of putting secrets where text goes.

Incident response when a secret hits a prompt

Confirm via request_id and finding categories (not by replaying raw logs)
Revoke and rotate the credential immediately
Scope which provider logs received the request
Notify stakeholders per your security policy
Add a regression test case with a redacted pattern variant

Detection is not prevention if alerts are ignored. Automate ticketing for unsafe verdicts in production.

Limitations

Regex and format-based secret detection:

Misses custom internal token formats without updated rules
Flags example keys in documentation (tune allowlists carefully)
Cannot determine if a key is valid or active without live verification (which you should not do casually in production scanners)
Does not scan binary attachments without text extraction

Obfuscation (s-k-test, base64-encoded .env files) may bypass naive scanners—re-scan after decoding transformations.