AID-H-037.001: Reasoning-Trace Confidentiality & Storage Controls

Version 1.20261015 · Actionable sub-technique

Classify raw reasoning traces, hidden chain-of-thought, scratchpads, and thinking summaries as sensitive state. Prevent raw display, ordinary logging, analytics export, or client-supplied hidden state. Store only approved summaries and integrity hashes by default. Allow raw content only through either a separate expiring break-glass path or a purpose-bound continuous monitor that processes it in protected memory or a short-lived encrypted buffer and exports only sanitized findings.

Pillars: app, model

Lifecycle: building, operation

Scope boundary

Owns classification, display prohibition, ordinary-log and analytics exclusion, approved summary or hash storage, separate expiring break-glass storage, and the isolated raw-data path required by an authorized continuous semantic monitor. It does not validate provider continuation blocks or decide whether a semantic finding exists.

AID-H-037.003: AID-H-037.001 controls whether and how reasoning content may be displayed or stored; AID-H-037.003 preserves exact provider-authored opaque blocks for continuation. A block may require byte-preserving round trip even when its plaintext is never exposed or retained.

AID-D-002.004: AID-H-037.001 protects raw reasoning content and approved storage; AID-D-002.004 observes bounded reasoning and thinking-token telemetry for anomalies without requiring raw chain-of-thought. Confidentiality controls must not force detectors to ingest hidden reasoning text.

AID-D-002.005: AID-H-037.001 authorizes and isolates the narrow raw-reasoning data path; AID-D-002.005 defines the semantic detector, coverage, model binding, and finding evidence. Continuous automated monitoring is purpose-bound processing, not a break-glass human-access exception.

Implementation guidance

AID-H-037.001-G001: Classify reasoning fields server-side, deny raw display and ordinary logging, reject client-supplied hidden state, and store only approved summaries or hashes outside break-glass.
AID-H-037.001-G002: Isolate continuous reasoning-trace monitoring from normal telemetry and retain raw content only in protected memory or a policy-bounded encrypted buffer.

AID-H-037.001-G001

Classify reasoning fields server-side, deny raw display and ordinary logging, reject client-supplied hidden state, and store only approved summaries or hashes outside break-glass.

Concept:

Extended-thinking systems can produce hidden reasoning blocks, summaries, signatures, or scratchpad-like metadata that should not be treated like ordinary user-visible text. The gateway should decide what can be displayed, what can be stored, what must be hashed only, and what must be round-tripped to the provider without client-side alteration.

Step 1: Define a reasoning-trace policy

{
  "policy_version": "2026.07",
  "default_display": "summary_only",
  "raw_reasoning_display": "deny",
  "store_raw_reasoning": false,
  "store_hash": true,
  "allow_client_supplied_reasoning_blocks": false,
  "maximum_summary_characters": 2000,
  "provider_round_trip": {
    "preserve_signed_blocks_exactly": true,
    "reject_modified_signed_blocks": true
  },
  "retention_days": 30
}

Step 2: Enforce display and storage controls at the AI gateway

# File: reasoning_security/trace_gate.py
from __future__ import annotations

import hashlib
import json
from dataclasses import dataclass
from typing import Any, Callable


@dataclass(frozen=True)
class ReasoningTraceDecision:
    display_text: str
    store_record: dict[str, Any]
    blocked: bool
    reasons: list[str]


VerifyBreakGlass = Callable[[dict, str], bool]
REQUIRED_POLICY = {
    "policy_version", "default_display", "raw_reasoning_display",
    "store_raw_reasoning", "store_hash", "allow_client_supplied_reasoning_blocks",
    "maximum_summary_characters", "provider_round_trip", "retention_days",
}


def sha256_text(value: str) -> str:
    return hashlib.sha256(value.encode("utf-8")).hexdigest()


def enforce_reasoning_trace_policy(
    response: dict[str, Any], *, policy: dict,
    break_glass_receipt: dict | None, verify_break_glass: VerifyBreakGlass,
) -> ReasoningTraceDecision:
    if set(policy) != REQUIRED_POLICY:
        raise ValueError("verified reasoning-trace policy schema mismatch")
    maximum_summary = policy["maximum_summary_characters"]
    retention_days = policy["retention_days"]
    if (
        not isinstance(maximum_summary, int) or maximum_summary < 0
        or not isinstance(retention_days, int) or retention_days < 0
    ):
        raise ValueError("verified reasoning-trace policy bounds are invalid")
    response_id = str(response["id"])
    raw_trace = str(response.get("reasoning_trace") or "")
    summary = str(response.get("reasoning_summary") or "")
    grant_valid = (
        break_glass_receipt is not None
        and verify_break_glass(break_glass_receipt, response_id)
    )
    reasons: list[str] = []
    if raw_trace and not grant_valid:
        reasons.append("raw_reasoning_trace_hidden")
    display_raw = (
        raw_trace
        and grant_valid
        and policy["raw_reasoning_display"] == "break_glass_only"
    )
    display_text = raw_trace if display_raw else (
        summary[:maximum_summary] if summary else "Reasoning summary not available."
    )
    store_record: dict[str, Any] = {
        "response_id": response_id,
        "policy_version": policy["policy_version"],
        "reasoning_trace_sha256": (
            sha256_text(raw_trace) if raw_trace and policy["store_hash"] else None
        ),
        "reasoning_summary": summary[:maximum_summary] if summary else "",
        "retention_days": retention_days,
        "break_glass_receipt_id": (
            str(break_glass_receipt["receipt_id"]) if grant_valid else None
        ),
    }
    if raw_trace and grant_valid and policy["store_raw_reasoning"]:
        store_record["raw_reasoning"] = raw_trace
    return ReasoningTraceDecision(display_text, store_record, False, reasons)


def reject_client_reasoning_state(
    request_body: dict[str, Any], *, forbidden_fields: set[str]
) -> None:
    supplied = sorted(key for key in request_body if key in forbidden_fields)
    if supplied:
        raise PermissionError(
            "client_supplied_reasoning_state_rejected:" + ",".join(supplied)
        )

Operational verification

Reconcile every eligible response, projection, storage, log, and analytics event. Test aliases, nested fields, client injection, serialization changes, break-glass expiry, sink failure, policy expiry, and provider-schema drift. A separately credentialed verifier reads back gateway, storage, and telemetry state; missing coverage is not PASS.

AID-H-037.001-G002

Isolate continuous reasoning-trace monitoring from normal telemetry and retain raw content only in protected memory or a policy-bounded encrypted buffer.

Create a purpose-bound monitor zone

Run the semantic monitor under a dedicated workload identity. Mount model and policy artifacts read-only; disable service-account token injection, shell/debug access, tools, workload credentials, training-data destinations, and general egress. Accept raw reasoning only from the approved gateway over mutually authenticated transport. The monitor may write raw trace and scores only to a separately authorized encrypted evidence store with a policy-defined short expiry; otherwise keep them in memory and erase the buffer after the result is acknowledged.

apiVersion: apps/v1
kind: Deployment
metadata:
  name: reasoning-monitor
  namespace: ai-security
spec:
  replicas: 2
  selector:
    matchLabels: {app: reasoning-monitor}
  template:
    metadata:
      labels: {app: reasoning-monitor}
    spec:
      automountServiceAccountToken: false
      enableServiceLinks: false
      securityContext:
        runAsNonRoot: true
        runAsUser: 65532
        runAsGroup: 65532
        fsGroup: 65532
        fsGroupChangePolicy: OnRootMismatch
        seccompProfile: {type: RuntimeDefault}
      containers:
        - name: monitor
          image: registry.corp.example/ai-security/reasoning-monitor@sha256:6d8c4cc693d6334447de38ce61f41f945b3e411844044fec618507d0960c14a3
          imagePullPolicy: IfNotPresent
          args: ["--listen=0.0.0.0:8443", "--tls-cert=/tls/tls.crt", "--tls-key=/tls/tls.key", "--client-ca=/tls/client-ca.crt", "--policy=/policy/monitor.json", "--model=/model", "--no-request-logging"]
          ports: [{name: mtls, containerPort: 8443, protocol: TCP}]
          resources:
            requests: {cpu: "1", memory: 4Gi}
            limits: {cpu: "2", memory: 8Gi}
          securityContext:
            allowPrivilegeEscalation: false
            capabilities: {drop: ["ALL"]}
            readOnlyRootFilesystem: true
            runAsNonRoot: true
            runAsUser: 65532
          volumeMounts:
            - {name: scratch, mountPath: /run/monitor}
            - {name: model, mountPath: /model, readOnly: true}
            - {name: policy, mountPath: /policy, readOnly: true}
            - {name: tls, mountPath: /tls, readOnly: true}
          readinessProbe:
            tcpSocket: {port: mtls}
            periodSeconds: 10
      volumes:
        - name: scratch
          emptyDir: {medium: Memory, sizeLimit: 64Mi}
        - name: model
          persistentVolumeClaim: {claimName: reasoning-monitor-model-ro}
        - name: policy
          projected:
            defaultMode: 0440
            sources:
              - secret: {name: reasoning-monitor-policy}
        - name: tls
          projected:
            defaultMode: 0440
            sources:
              - secret: {name: reasoning-monitor-mtls}
---
apiVersion: networking.k8s.io/v1
kind: NetworkPolicy
metadata:
  name: reasoning-monitor-default-deny
  namespace: ai-security
spec:
  podSelector:
    matchLabels: {app: reasoning-monitor}
  policyTypes: [Ingress, Egress]
  ingress:
    - from:
        - namespaceSelector:
            matchLabels: {kubernetes.io/metadata.name: ai-serving}
          podSelector:
            matchLabels: {app: inference-gateway}
      ports: [{protocol: TCP, port: 8443}]
  egress: []

Replace the example image reference only with the signature-verified digest produced by the organization's release pipeline; admission must reject a tag or an unapproved digest. The TCP readiness probe proves only that the dedicated mTLS listener accepts connections; use a separate authenticated synthetic request from the gateway to verify the monitor contract end to end without adding an unauthenticated health route.

Constrain every sink

The gateway releases to the general SIEM only label, severity, digest, and a restricted evidence reference. Configure logging filters and OpenTelemetry processors to reject the raw field and monitor scores by exact key, including aliases and nested forms. Keep break-glass human access separate from this automated route. Reconcile all eligible traces, buffer deletion or expiry receipts, monitor requests, results, and sanitized SIEM events. Test pod exec denial, credential absence, blocked egress, log and crash-dump leakage, partial streams, oversized input, monitor restart, buffer expiry, evidence-store outage, and SIEM schema rejection. Missing isolation or deletion evidence is not PASS.

Action: Route raw reasoning only through this isolated monitor path, export only the permitted sanitized fields, and do not claim PASS when isolation or buffer-deletion evidence is missing.

Open-source tools

OpenTelemetry
Microsoft Presidio
Open Policy Agent (OPA)
LiteLLM

Commercial / hosted tools

Datadog (reasoning-state evidence only; not exposure enforcement)
Splunk Enterprise Security (reasoning-state evidence only; not exposure enforcement)

Threat mappings

MITRE ATLAS

AML.T0057 LLM Data Leakage
AML.T0056 Extract LLM System Prompt

MAESTRO

Data Leakage through Observability (L5)

OWASP LLM Top 10 2026

LLM02:2026 Sensitive Information Disclosure
LLM08:2026 Hidden Context Exposure (raw reasoning controls reduce reconstruction of non-user-facing instructions and policy context, while generic server-log leakage remains outside LLM08)

OWASP ML Top 10 2023

OWASP Top 10 for Agentic Applications 2026

NIST Adversarial Machine Learning 2025

NISTAML.036 Leaking information from user interactions

Cisco Integrated AI Security and Safety Framework

AISubtech-14.1.2 Insufficient Access Controls (Enforces the stated application access boundary; hosting infrastructure is a separate scope)
AISubtech-16.1.1 Logging Sensitive Conversations
AITech-16.1 Eavesdropping
AITech-4.2 Context Boundary Attacks (rejects client-supplied hidden reasoning state at the gateway)
AITech-7.1 Reasoning Corruption (rejects client-supplied hidden reasoning state at the gateway)
AITech-8.2 Data Exfiltration / Exposure
AITech-8.3 Information Disclosure

Google Secure AI Framework 2.0 - Risks

SDD: Sensitive Data Disclosure

Databricks AI Security Framework 3.0

Model Serving - Inference response 10.6: Sensitive data output from a model