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Secure Jev as an untrusted decision component, not as a security boundary. Keep its API key on a server, send only redacted state, place explicit policy in versioned criteria, test hostile inputs, validate every typed answer and confidence value before acting, and fail closed for privileged operations. Pin the model version, log enough context to reproduce a decision without retaining secrets, and retry only documented transient failures with an idempotency key.
Jev evaluates a state such as a ticket, chat transcript, tool call, text, or JSON object against typed questions and returns structured answers that application code can branch on. That structure makes integration predictable, but it does not prevent user-controlled content from influencing the result. Your threat model therefore has to include prompt injection, adversarial suffixes, privacy leakage, version drift, API failure, and the blast radius of an incorrect automated action.
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What Jev does and where trust ends
Jev is a decision API rather than a text-generation service. A request supplies one state and up to eight typed questions. Each question can request a Choice, Score, or Noul answer. The production endpoint is https://jevmodel.org/v1/systemone, called with Bearer authentication and, optionally, an Idempotency-Key.
The useful security boundary is the policy-validation layer around Jev, not Jev itself. Treat the complete flow as:
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- Untrusted user, tenant, document, or tool content enters your application.
- A state-construction and redaction layer selects and normalizes fields.
- Your server crosses the Jev API boundary.
- Jev returns typed answers and probability or confidence fields.
- A policy-validation layer checks the response and authorization context.
- Only then does the system enqueue a job, call a tool, change data, or request human review.
The assets in that flow include API keys, state payloads, question instructions, criteria and policy text, model and version selection, returned decisions, confidence values, logs and traces, billing balance, and every privileged downstream action. A schema-valid response protects the shape of your program input; it does not prove that the answer was correct, fair, uninfluenced, or safe to execute.
Build the threat model before integrating
| Boundary or asset | Threat to consider | Required control |
|---|---|---|
| Browser or tenant to your server | A user injects instructions into a ticket, chat, JSON field, attachment, or tool result. | Authenticate the caller, authorize the tenant, normalize input, cap size, and mark user content as data rather than policy. |
| State-construction layer | Secrets, payment data, session cookies, or another tenant’s records are copied into state. | Use an allowlist of fields, redact sensitive values, enforce tenant scoping, and test serialization. |
| Question and criteria text | A developer changes policy informally, or an attacker influences criteria through configuration. | Store criteria as versioned policy, require code review, hash the exact text, and deploy it like code. |
| Jev API credential | Key theft from frontend bundles, repositories, crash reports, CI logs, or support tickets. | Keep the key in a server-side secret manager, restrict access, rotate on exposure, and never print it. |
| Returned decision | An answer has the right enum or numeric type but is semantically unsafe. | Validate type, allowed values, range, policy version, tenant authorization, and required evidence before side effects. |
| Confidence or probability | A high score is mistaken for proof, calibration, or authorization. | Use conservative thresholds, maintain a review band, monitor outcomes, and deny or defer privileged actions when uncertain. |
| Downstream tool or action | A manipulated decision triggers deletion, payment, account change, message sending, or code execution. | Use explicit capability allowlists, human approval or deny-by-default, idempotent jobs, and independent authorization checks. |
| Logs, traces, and billing | Retained state exposes private data, or abuse consumes quota and balance. | Redact and minimize logs, define retention, rate-limit callers, alert on unusual volume, and separate audit access. |
| Model and client supply chain | A rolling model, compromised update, dependency, or file connector changes behavior. | Pin versions where possible, verify signed updates and checksums, restrict file access, and review release changes. |
Secure the integration step by step
1. Isolate and rotate the credential
Put the Jev key in a server-side secret manager or a protected worker environment. Browser JavaScript, mobile binaries, public repositories, screenshots, ordinary application logs, and client-visible error responses are not acceptable storage locations. The Jev documentation is explicit: “Never ship a key in client-side code or commit it to a repository — if a key leaks, revoke it and mint a new one.” Give separate environments separate keys and restrict which service identities can read them.
When exposure is suspected, revoke the key immediately, mint a replacement, inspect access logs, and determine whether the old key was used for unauthorized requests. Do not wait for a code fix before revocation; a leaked bearer token can be replayed by anyone who obtains it.
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Build state from an explicit field allowlist instead of forwarding an entire database record or conversation. Include only facts needed for the question. Remove secrets, payment-card data, session cookies, authentication headers, internal notes, hidden system prompts, and records belonging to another tenant. Normalize encodings and cap lengths before serialization.
Minimization reduces both privacy impact and attack surface. Every extra field gives an attacker another place to smuggle instructions or contradictory policy text. Keep a redacted representation for diagnostics and retain the original only where your legal and operational requirements justify it.
3. Make criteria executable policy
Write the question criteria so that each allowed outcome has an unambiguous meaning. State which fields are evidence, which text is untrusted, what to do when evidence is missing, and which cases require review. Do not rely on an informal instruction such as “be sensible.” A policy should define the decision boundary your application will enforce after the response arrives.
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Store the exact criteria text with a version identifier and cryptographic hash. Require review for changes, test old and new versions against the same fixtures, and include the hash in audit records. This prevents a later policy edit from making an earlier decision impossible to explain.
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Send requests only from a backend or trusted worker over HTTPS. A minimal wire-level shape is:
POST https://jevmodel.org/v1/systemone
Authorization: Bearer $JEV_API_KEY
Content-Type: application/json
Idempotency-Key: <unique-request-id>
{"state": {"ticket_text": "..."}, "questions": [{"type": "Choice", "question": "...", "criteria": "..."}]}Use a fresh idempotency value for each logical operation and preserve it when retrying that same operation. Generate it before the first attempt and store it with your job record. Never put the key, authorization header, or unredacted state into a client-facing response.
5. Validate the response before any side effect
Validation should be deterministic application code. Check all of the following before branching:
- The response parses and matches the expected top-level schema.
- Each answer has the requested type: Choice, Score, or Noul.
- Choice values are in your allowlist; scores are numeric and within the range your policy defines; Noul is handled explicitly rather than treated as approval.
- The response identifies the request and, where available, the resolved model version.
- The criteria or policy hash matches the version selected for this job.
- The tenant, subject, and authorization context still match the record that triggered the request.
- Required evidence fields are present and were not redacted into an unusable value.
- Confidence or probability is present, numeric, and handled by a threshold policy rather than displayed as a guarantee.
Reject malformed, incomplete, out-of-range, or context-mismatched responses. A response that passes JSON-schema validation but fails a business rule must not reach a privileged tool.
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Use Jev to classify or recommend, then let an independent policy layer authorize. For example, a “likely refund” Choice can route a case to a refund policy, but it should not itself authorize payment. Account deletion, credential changes, legal notices, code execution, external messages, and other irreversible actions should require a human approval step or a deny-by-default branch when confidence, evidence, or authorization is insufficient.
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Keep tool capabilities narrow. Pass only the fields a tool needs, validate its arguments again, and make the action idempotent where possible. Record who or what approved the action and whether a human overrode the model.
7. Log for audit without creating a data leak
For each decision, record a redacted input reference, tenant and subject identifiers, request ID, idempotency key, resolved model version, criteria or policy hash, answer types and values, confidence or probability, threshold branch, latency, HTTP status, retry count, downstream action, and any human override. Store the minimum state needed to reconstruct the decision; use a secure pointer to the source record when full content is not necessary.
Protect audit logs with access controls and retention limits. Do not log bearer tokens, complete payment details, session material, or unrestricted user transcripts. Make redaction happen before tracing and error serialization so a debug path cannot bypass it.
Defend against prompt injection and adversarial state
User-controlled state can influence Jev decisions. The October 4, 2026 arXiv paper “Hidden Risks of Jev” reports evaluations covering prompt injection, adversarial suffixes, supply-chain poisoning, membership and private-attribute inference, model extraction, jailbreak and harmful-content detection, and dual-use scenarios. Its abstract concludes that Jev remained vulnerable to the examined security and privacy threats while its decision capability could support both beneficial and malicious uses.
Design tests around the exact policy you operate. Include fixtures in which a ticket or document:
- Orders the system to ignore the question criteria or reveal hidden instructions.
- Claims to be an administrator and requests an unauthorized action.
- Contains policy text that contradicts the criteria supplied by your application.
- Uses an adversarial suffix, unusual Unicode, encoded text, or nested JSON to alter interpretation.
- Floods the state with irrelevant content intended to push required evidence out of context.
- Combines true evidence with a fabricated approval, forged tool result, or cross-tenant identifier.
- Attempts to elicit a private attribute or infer whether a person appeared in prior data.
Run these tests in staging and in continuous regression suites whenever criteria, model versions, serializers, or client dependencies change. Test both the model response and your validator: the secure outcome is not merely “Jev rejected the attack,” but “the whole system withheld the dangerous side effect.”
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Keep policy text and evidence structurally distinct in your state representation. Delimit untrusted fields, label their provenance, and avoid concatenating user text into the criteria string. If an input is ambiguous or hostile, route to review rather than trying to persuade the model with increasingly elaborate instructions.
Use confidence without over-trusting it
A confidence or probability field is a signal for routing, not proof of truth, authorization, or calibration. A high value can accompany a manipulated or incomplete state. Define at least three application outcomes: automatic handling for narrowly bounded, low-impact cases; human review for an uncertainty band or missing evidence; and deny or hold for privileged actions.
Choose thresholds using labeled examples from your own workflow, measure false approvals and false denials, and monitor drift after model or policy changes. Preserve the threshold values and the branch selected in each audit record. Never let a caller override thresholds through user-controlled state.
The Jev CLI warns that the jev-latest alias can move without notice and that outputs are not bit-for-bit repeatable. Compare threshold bands and business outcomes rather than exact floating-point values. For workflows that require stable behavior, pin a versioned model and include the resolved version in every record.
Handle errors, retries, and unknown outcomes
Classify failures before deciding whether to retry:
| Condition | Action | Why |
|---|---|---|
| HTTP 429 rate limit | Retry with exponential backoff and jitter, preserving the same idempotency key; enforce a maximum attempt count. | The request may succeed later, but uncontrolled retries amplify load. |
| HTTP 502 transient gateway failure | Use the same bounded backoff and idempotency behavior as for 429. | A gateway error can be temporary, but the original operation must remain deduplicated. |
| Timeout or connection loss | Mark the result unknown, retry only within your deadline with the same idempotency key, then route to review or a safe hold. | You cannot know whether the server processed the request. |
| HTTP 401 authentication failure | Stop automatic retries, alert the operator, and verify or rotate credentials. | Repeated attempts will not repair an invalid or revoked key. |
| HTTP 402 billing or balance failure | Stop retries and send an operational alert. | The request cannot succeed until the account condition is corrected. |
| HTTP 422 validation failure | Quarantine the job, capture a redacted diagnostic, and fix the request or criteria. | Retrying an invalid payload repeats the same failure. |
| Malformed or policy-invalid response | Do not act; preserve the audit event and route to review or deny. | Shape correctness is not enough for a safe decision. |
Use exponential delays with jitter, a total time budget, and a dead-letter or review queue. Alert on retry storms, rising 401 or 422 rates, unusual 429 volume, and sudden shifts in confidence or outcome distribution. Treat an exhausted retry budget as an unknown decision, never as approval.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design around the documented limits
The current Jev Model documentation lists these limits. They are operational constraints, not a promise that limits will never change, so monitor rejection rates and capacity headroom.
| Limit | Value | Design implication |
|---|---|---|
| Questions per request | Up to 8 | Group related checks carefully; split larger workflows into separately authorized jobs. |
| Serialized state | 1,000,000 characters | Summarize or select fields before sending; do not use the maximum as a target. |
| Instruction text per question | 1,800 characters | Keep criteria precise and versioned; move long policy explanations into application code and tests. |
| Serialized criteria per question | 2,000 characters | Use concise, testable rules and reject oversized configurations before deployment. |
| Rate limit | 120 requests per minute per key | Queue bursts, apply per-tenant quotas, and alert before saturation. |
| Image assets on the experimental image/Box surface | Up to 4 per request | Count assets before submission and define a review path when an input exceeds the limit. |
Hosted API and jev-cli/local workflows
The choice of hosted API versus a local workflow changes the trust boundary; it does not remove the need for policy validation.
| Deployment concern | Hosted Jev API | jev-cli or local workflow |
|---|---|---|
| Data residency and exposure | State crosses the hosted API boundary; residency details are not stated in the available documentation. | Local preprocessing can keep raw files on your infrastructure, but any remote model call still creates an external boundary. |
| Credential and network boundary | Server-side Bearer key, HTTPS, documented endpoint, and idempotent retries. | Local key handling remains your responsibility; the README says the CLI contacts only the TypeSafe API and GitHub Releases. |
| Version control | Pin a versioned model where the workflow requires stable behavior; rolling aliases can change. | Pin CLI and dependency versions and record the model version selected by the workflow. |
| Validation and dry run | Implement request and response validation in your service. | Use the CLI’s offline validate checks and --dry-run support before sending or applying changes. |
| File and MCP access | Expose only the state fields your service constructs. | Keep MCP file access deny-by-default and review every allowed path. |
| Rate and retry semantics | Documented limits include 120 requests per minute per key; retry 429 and 502 with idempotency and backoff. | Local validation does not eliminate hosted rate limits when the CLI calls the API. |
| Observability | Log request IDs, versions, policy hashes, responses, thresholds, and overrides in your own audit system. | The README states that the CLI exposes no telemetry and redacts keys; you still need application-level audit records. |
| Supply chain | Review API-client dependencies and deployment images. | Verify signed updates and checksums, require HTTPS certificate verification, and note that the CLI never follows redirects. |
| Latency and cost | Network latency, request limits, and account balance affect throughput; exact pricing and latency are not stated here. | Local checks can reduce invalid submissions; model-call latency and account costs still apply when invoking the hosted service. |
| Blast radius | A compromised service key or validator can affect every tenant reachable by that service. | A local tool may narrow file scope, but an over-broad automation account can still cause privileged damage. |
Harden the client and supply chain
Run offline validation before deployment and use a dry run to inspect which files, questions, criteria, and state fields would be submitted. Keep file access deny-by-default for MCP integrations; grant only the paths and operations required by one workflow. Separate development, staging, and production credentials and restrict who can invoke production actions.
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Verify signed updates and checksums for the CLI and its dependencies. Require HTTPS certificate verification, review dependency and release changes, and pin versions in build manifests. The CLI documentation says it exposes no telemetry, redacts keys, and never follows redirects; preserve those properties by avoiding wrappers that re-enable unrestricted telemetry, log secrets, or silently follow a different host.
Monitor behavior after launch
Security controls are incomplete without detection. Establish baselines for request volume, response latency, 429 and 502 rates, malformed responses, confidence distributions, Choice frequencies, review rates, and downstream action counts. Segment metrics by tenant, workflow, model version, and policy hash so a local regression is visible.
Alert on a new model version, criteria-hash change, sudden increase in Noul or low-confidence answers, unusual cross-tenant access attempts, repeated authorization failures, and any action that bypasses the validator. Sample redacted decisions for human quality review and compare them with eventual outcomes. Keep a kill switch that disables automatic side effects while allowing evidence-preserving review traffic to continue.
Quick Recap
Incident response for a bad or exposed decision
- Contain: disable the affected automation route, revoke exposed keys, and block the suspected tenant, workflow, or model version if necessary.
- Preserve: retain redacted request IDs, policy and model hashes, response values, confidence, retries, logs, and downstream action records.
- Assess: identify which inputs, tenants, decisions, and side effects were reachable; distinguish an API outage from an injection or credential event.
- Correct: patch state construction, criteria, validators, permissions, or dependencies; add a regression fixture for the exact failure.
- Recover: rotate credentials, replay only idempotent and authorized jobs, and require human approval for remediation actions.
- Learn: update the threat model, thresholds, monitoring, and runbooks, then document the policy and model versions involved.
Production readiness checklist
- The API key exists only in a protected server or worker secret store and has a rotation procedure.
- State is assembled from an allowlist, redacted, tenant-scoped, size-limited, and free of secrets and unrelated records.
- Questions and criteria define allowed outcomes, missing-evidence behavior, and review conditions; their versions and hashes are recorded.
- Hostile-input fixtures cover instruction override, forged authority, contradictory policy, adversarial suffixes, context flooding, and privacy inference.
- Every response is checked for schema, type, enum or range, policy hash, model version, authorization context, evidence, and confidence handling.
- Privileged actions require independent authorization and human review or a deny-by-default branch when uncertainty exists.
- 429 and 502 retries use exponential backoff, jitter, bounded attempts, and the same idempotency key; 401, 402, and 422 are not blindly retried.
- Timeouts, malformed outputs, and exhausted retries become explicit unknown or hold states.
- Audit records contain redacted provenance, request and policy identifiers, thresholds, outcomes, and overrides without bearer tokens or unnecessary personal data.
- Model versions are pinned where stability matters, rolling aliases are monitored, and exact floating-point equality is not used as a reproducibility test.
- Documented limits are monitored: eight questions, 1,000,000 state characters, 1,800 instruction characters, 2,000 criteria characters, 120 requests per minute per key, and four experimental image assets.
- CLI validation, dry runs, signed updates, checksums, HTTPS verification, dependency review, and deny-by-default MCP file access are part of the release process.
- A kill switch, review queue, incident runbook, and key-revocation procedure have been tested before production traffic is enabled.
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