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Preserve an AI failure as a chain-of-custody package, not a screenshot or paraphrase. Freeze the exact serialized request, rendered prompt, conversation and tool context; record the requested and served model, every behavior-changing setting, and the complete raw response; hash and timestamp the original artifacts; control and document access; then write a readable incident narrative that links each conclusion to immutable originals. That package lets another investigator distinguish a prompt change, model rollout, retrieval difference, tool failure, post-processing defect, or ordinary stochastic variation.
What makes an AI bug report reproducible?
Reproducibility means a reviewer can reconstruct the same inputs, configuration, execution context, and observed result without relying on someone’s memory. A paraphrased prompt, a screenshot, or a model name by itself cannot do that. The evidence must show what was sent, what the service actually served, what context it received, what it returned, and how the records were handled afterward.
This is a provenance problem. NIST defines provenance as “the chronology of the origin, development, ownership, location, and changes to a system or system component and associated data.” For an AI incident, that chronology includes people and services that edited a prompt, selected a deployment, added retrieved documents, called tools, transformed the output, or exported the record.
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Keep two connected layers:
- Raw evidence: the original request and response bytes, files, tool payloads, retrieval records, logs, and metadata, retained in an immutable or append-only location.
- Analysis: a human-readable account of symptoms, observations, hypotheses, reproduction attempts, and unresolved questions. Analysts may annotate this layer, but they do not overwrite the raw capture.
The minimum evidence bundle
Create one case package for each suspected defect. The following fields cover the minimum needed to investigate a wrong answer, refusal, unsafe action, formatting regression, latency spike, or tool-use failure.
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| Evidence area | Capture | Why it matters |
|---|---|---|
| Case identity and custody | Incident ID, reporter, investigator, affected product or endpoint, environment, UTC timestamps, and access or export history | Establishes which event the files belong to and who handled them |
| Exact input | System and developer messages, user messages, rendered template variables, attachments, locale, conversation order, and the original serialized request | Shows the actual model input rather than an edited reconstruction |
| Model identity | Provider, requested model name, served snapshot or deployment ID, API and SDK versions, and request or correlation ID | Separates a model substitution or rollout from a prompt defect |
| Generation settings | Temperature, top-p or equivalent sampling controls, maximum tokens, stop sequences, response format, safety settings, feature flags, and tool definitions | These controls can change behavior even when the prompt is unchanged |
| Supplied context | Tool calls and returned payloads, retrieved document IDs and scores, memory state, files, and relevant external responses | Retrieval, tools, memory, and files are part of the effective input |
| Exact result | Raw response bytes, structured output, refusals or safety messages, tool-call arguments, error payloads, latency, token counts, and finish reason | Preserves both what the model said and how the service completed the request |
| Integrity and handling | Cryptographic hashes, canonicalization rules, timestamps, signatures or append-only entries where feasible, retention class, redaction status, and access history | Shows that records were not silently altered and makes later verification possible |
| Analysis layer | Symptoms, observed facts, hypotheses, reproduction steps, comparisons, and unresolved questions, each linked to raw artifact identifiers | Allows review without confusing interpretation with evidence |
Do not treat a missing field as harmless. Mark it explicitly as unavailable and explain why. A reviewer can then distinguish an unrecorded setting from a setting that was deliberately absent.
Freeze the case before rerunning the failure
Rerunning first can destroy the most valuable evidence: the original model deployment may roll forward, a retrieval index may change, or a temporary tool response may disappear. Create the case and preserve the original transaction before trying to reproduce it.
- Create a unique case ID. Record who opened the case, the UTC time, the affected endpoint or product, the environment, and the initial symptom in one short sentence.
- Export the original request and response exactly as received. Keep the raw bytes, including field ordering where the transport preserves it, escaped characters, headers or metadata that identify the request, and error payloads. Do not replace the original with a cleaned transcript.
- Make a separate normalized view. Convert the exchange to a consistent, searchable representation for analysis, but label it as derived and retain a link to the raw files.
- Pin prompt and configuration versions. Save the template version, fully rendered variables, system and developer instructions, and any feature-flag state. Prompt-management guidance from OpenAI describes version history, rollback, prompt IDs, variables, and linked evaluations; those identifiers belong in the case record when they are available.
- Preserve sequence and context. Keep every conversation turn in order and append later messages rather than rewriting earlier ones. Prompt-caching guidance likewise emphasizes preserving conversation history, because changing an earlier turn changes the effective input.
- Capture dependencies immediately. Export tool requests and responses, retrieval results, document identifiers and scores, memory records, uploaded files, and external service responses while they can still be obtained.
Capture the exact input, not a summary
System, developer, and user messages
Store each message with its role, content parts, order, and any metadata used by the endpoint. If a message contains text, images, audio, or files, preserve the original representation and the reference to the binary object. Record the locale, time zone, and conversation identifier because formatting, dates, and routing can depend on them.
Save both the prompt template and its rendered result. A template such as {{customer_name}} is not evidence of what the model saw; the case needs the concrete value inserted at runtime, including whitespace, escaping, truncation, and conditional sections. Keep the template revision or prompt ID beside the rendered prompt so an investigator can tell whether a later rerun used the same source.
The original serialized request
The serialized request is the authoritative input artifact. It captures fields that a transcript often omits: response-format declarations, seed or sampling controls when supported, tool schemas, metadata, and vendor-specific options. Preserve the transport envelope or a metadata record that identifies the endpoint, request or correlation ID, and client version. If headers contain secrets, store a restricted original and a redacted analyst view rather than deleting the original without a custody note.
Conversation history, tools, and retrieval
Conversation history is not background decoration. Earlier turns can establish instructions, facts, or memory that change the answer. Preserve every turn that was sent, including assistant messages inserted by the application and any truncation decision made to fit a context limit.
For tools, record the definition offered to the model, the exact arguments it generated, the application’s execution request, the returned payload, timestamps, errors, and any post-processing before the next model call. A wrong answer may originate in a tool response even when the model call is unchanged.
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Prove which model and settings ran
The requested model name is only one part of model identity. Hosted services can route a request to a dated snapshot, deployment, region, or silently updated alias. Record all of the following together:
| Category | Fields to retain |
|---|---|
| Provider and endpoint | Provider name, service endpoint, region or deployment target when exposed, operation name, and request or correlation ID |
| Requested identity | Model name or alias supplied by the client, version string, and deployment selected by application configuration |
| Served identity | Model snapshot, concrete deployment ID, revision, or response metadata that identifies what actually processed the request |
| Client stack | API version, SDK and runtime versions, application build, and relevant proxy or gateway version |
| Sampling and limits | Temperature, top-p or equivalent controls, maximum output tokens, stop sequences, seed when supported, and penalties or frequency controls |
| Output and safety | Response format or schema, safety settings, moderation mode, refusal policy, and content filters |
| Feature state | Feature flags, routing rules, structured-output mode, caching state when relevant, memory configuration, and tool definitions |
Record a value of “not set” when a field was omitted, rather than inferring a provider default. If the service does not expose the served snapshot, preserve the response metadata that was available and state that the concrete revision could not be verified. That limitation is itself important evidence in a regression report.
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Capture the complete result
Save the response exactly as returned before application formatting, filtering, redaction, translation, or database storage. Keep the raw bytes and a parsed view. The parsed view should identify:
- Every text or multimodal content part, in order.
- Structured-output fields and validation errors.
- Refusal, safety, policy, or moderation messages.
- Tool-call names and arguments, including malformed arguments.
- Provider and application error payloads, HTTP or transport status, and retry details.
- Finish reason, token counts, latency measurements, and timestamps for request, first byte, completion, and downstream processing when those are available.
Keep intermediate results when one user action causes several model calls. A final answer can hide an earlier refusal, tool failure, retry, or fallback model. Give each call its own artifact ID and record the parent transaction that links them.
Hash, timestamp, and protect the originals
Canonicalization and hashes
Compute a cryptographic digest for each raw request, response, attachment, tool payload, and retrieval manifest. Also compute hashes for canonical request and response representations used in comparisons. Document the algorithm, character encoding, line-ending rules, field-order rules, treatment of absent versus null fields, and the UTC time of hashing. Without those rules, two investigators may hash different serializations of the same logical record and draw the wrong conclusion.
Store the digest beside the artifact, not only in a separate spreadsheet. A signed or append-only log can strengthen the record by making later deletion or replacement visible. It does not replace the underlying files: a hash proves that bytes match a prior digest, not that the captured bytes were truthful or complete when first collected.
Storage, access, and recovery
Use access-controlled storage with write protection or append-only behavior for the original package. Record every read, export, redaction, deletion request, and transfer. Keep an offline export for disaster recovery and periodically recompute hashes against it. A hardware-encrypted USB flash drive can be useful for transporting that export, but encryption alone is not proof of integrity; the hashes, custody log, and access controls remain necessary.
Separate restricted originals from analyst views
Prompts and outputs can contain credentials, personal data, customer records, or proprietary documents. Maintain an access-restricted original and a redacted analyst copy. OWASP AISVS recommends avoiding default raw-content capture when it could leak secrets and preserving hashes, scores, IDs, and source metadata unless a documented trigger permits full text. Document who approved any full-text access, what was removed from the analyst copy, and whether redaction changed the bytes covered by the original hash.
Write an incident narrative that does not contaminate evidence
The readable report should help a reviewer understand the failure without making the reviewer trust an unverified summary. Organize it into four labeled parts:
- Observed facts: what the user saw, which artifact records it, and the exact UTC time or transaction ID.
- Reproduction steps: pinned inputs, model identity, settings, environment, and the sequence used to rerun the case.
- Hypotheses: possible causes such as a prompt revision, model rollout, retrieval change, tool payload, or post-processing defect. Mark each as unconfirmed until evidence supports it.
- Unresolved questions: missing served-model metadata, unavailable external responses, uncertain truncation, or any other limit on the conclusion.
Link every statement to an artifact ID, hash, log entry, or external record. Never “correct” a raw response because it looks malformed; annotate the problem in the analysis layer and retain the malformed original.
Reproduce the failure with controlled changes
Start with a rerun that uses the same pinned prompt, conversation, model deployment, settings, tools, retrieval snapshot, files, locale, time zone, client version, and feature flags. Compare raw artifacts, not screenshots or summaries. A different output under identical recorded conditions may be expected when sampling is nondeterministic; record the variation rather than selecting the run that best supports a theory.
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After the baseline, create a comparison matrix in which each run changes one axis while all other captured inputs remain fixed.
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| Axis | Questions to answer |
|---|---|
| Prompt | Did the template revision, rendered variables, message order, hidden developer context, or truncation change? |
| Model | Did the provider, requested name, served snapshot or deployment, API version, or rollout date change? |
| Configuration | Did sampling, token limits, response format, safety controls, tools, retrieval, memory, or feature flags change? |
| Environment | Did locale, time zone, client or SDK, network dependency, external service, or underlying data change? |
| Behavior | Did the exact text, tool action, refusal or error type, latency, token usage, or finish reason change? |
| Evidence handling | Are hashes, timestamps, signatures, access history, redaction status, and retention status present for every run? |
For each comparison, retain the full artifact set and state the controlled variable in the run manifest. If several variables changed together, the result can show correlation but cannot identify the cause.
Distinguish regression from nondeterminism
A regression is a behavior change associated with a changed model, prompt, configuration, environment, or dependency. Stochastic variation is a different output under effectively identical conditions. You need repeated controlled runs and complete settings to separate them. If the provider does not expose a deterministic seed or served revision, say so and report the observed distribution of outcomes rather than claiming certainty.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Meet audit and retention obligations
The UK Government Code of Practice for the Cyber Security of AI states that developers should document and create an audit trail for the AI system and that system operators should log system and user actions to support security compliance, incident investigations, and vulnerability remediation. In practice, that means logging changes to system prompts and model configuration as well as the actions that created, viewed, exported, or deleted evidence.
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There is no universal retention period prescribed for this evidence. Set a period using incident-response needs, privacy and data-protection rules, legal holds, contractual commitments, and the expected lifetime of the affected product. Publish the rule, assign a retention class to each artifact, and preserve access, export, and deletion events. If a legal hold or active investigation extends retention, record who approved the extension and what scope it covers.
Restrict access by role and need. Keep the original and analyst views under the same case ID, but do not grant every investigator access to sensitive prompt content. Review access logs during closure and document the final disposition of each artifact.
A practical case-package layout
A predictable layout reduces accidental omission. One workable arrangement is:
case-INC-2026-001/
manifest.json
raw/
request-0001.bin
response-0001.bin
tool-0001-request.bin
tool-0001-response.bin
retrieval-0001.json
attachments/
normalized/
exchange-0001.json
hashes/
sha256.txt
redacted/
exchange-0001.html
analysis/
incident-report.html
reproduction-runs.csv
custody/
access-log.jsonl
export-log.jsonlThe names are illustrative; the important properties are stable identifiers, a manifest linking related calls, raw and derived views kept separate, and a custody record that can be audited.
Manifest fields
At minimum, the manifest should identify the case, artifact IDs, creation and collection times in UTC, source endpoint, requested and served model identity, client and API versions, settings, prompt version, environment, redaction status, retention class, hash algorithm, canonicalization rules, and the person or service that collected each file. Include parent-child links when one user action produced multiple model calls.
Common evidence failures and their fixes
| Failure | Why it breaks investigation | Fix |
|---|---|---|
| Only a paraphrased prompt was saved | Wording, hidden instructions, variables, whitespace, and message order cannot be reconstructed | Retain the serialized request and fully rendered messages, with template revision metadata |
| Only the model alias was recorded | An alias may route to a different snapshot or deployment later | Capture served revision or deployment metadata and the request or correlation ID |
| Tool and retrieval context was omitted | The effective input and external dependencies are missing | Save tool schemas, arguments, returns, document IDs, scores, index version, and supplied content or protected references |
| A screenshot replaced raw output | Formatting, hidden fields, errors, token counts, and tool calls are lost | Keep raw response bytes and a parsed representation; use screenshots only as supplemental evidence |
| Analysts edited the original file | It becomes impossible to prove what the service returned | Make annotations in a separate analysis layer and hash the untouched original |
| Secrets were copied into broad logs | Evidence collection creates a new security incident | Restrict originals, produce redacted analyst views, and document approvals and redaction status |
| Several settings changed in one rerun | The comparison cannot identify a cause | Return to the last pinned baseline and change one axis per run |
| No retention or access rule exists | Records may be deleted, over-retained, or accessed without accountability | Assign a documented retention class and log reads, exports, redactions, and deletion events |
What to report when evidence is incomplete
Do not fill gaps with assumptions. State the strongest conclusion supported by the package and its boundary. For example, you may be able to show that the rendered prompt and response differ across two dates while being unable to prove the served model revision. Report a behavior change associated with the date and configuration records, then identify served-model provenance as unresolved. Similarly, a retrieval document ID and hash can establish that content changed without exposing the document text.
When raw content cannot be retained, preserve the reason, approving authority, digest, stable IDs, scores, timestamps, and source metadata. A restricted original may be held under a separate privacy or legal control, while the analyst copy supports most technical comparisons.
Final pre-submission checklist
- Case ID, reporter, investigator, endpoint, environment, and UTC collection times are present.
- The exact serialized request and response are preserved, with normalized copies clearly marked as derived.
- System, developer, user, and conversation messages include rendered variables, attachments, locale, and order.
- Provider, requested model, served snapshot or deployment, API and SDK versions, and request ID are recorded.
- Sampling, token, stop, response-format, safety, tool, memory, retrieval, and feature-flag settings are captured, including explicit “not set” values.
- Tool calls, returned payloads, retrieval IDs and scores, files, and external responses are linked to the model call.
- Raw outputs include refusals, errors, tool arguments, latency, token counts, and finish reason where available.
- Canonicalization rules, cryptographic hashes, timestamps, signatures or append-only records, and offline verification are documented.
- Restricted originals and redacted analyst views are separated, with access and redaction history.
- Every analysis claim points to an artifact, hash, or log entry; hypotheses and unresolved questions are labeled.
- Reproduction runs use pinned inputs and change one variable at a time.
- Retention, legal-hold, export, and deletion rules are recorded and followed.
Following this checklist turns an AI bug report into evidence another team can verify, rather than a narrative that depends on trust.
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