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Secure Jev as an untrusted decision component, not as a security boundary. Keep credentials and policy logic on a trusted server, minimize and sanitize the state you send, test hostile inputs, validate every typed answer and confidence value before acting, pin versions, and fail closed for privileged operations. The production endpoint is POST https://jevmodel.org/v1/systemone; your surrounding application is responsible for protecting everything on either side of that call.
What Jev does—and what it does not guarantee
Jev evaluates one application state—such as a ticket, chat transcript, tool call, text value, or JSON object—against typed questions. It returns structured answers that application code can branch on rather than a block of generated prose. A request can contain up to eight questions, using Choice, Score, or Noul answer types.
That structure makes integration easier to test, but it does not make the input trustworthy. User-controlled text is part of the state, and that text can influence the returned decision. A valid-looking enum, score, or confidence field proves only that the response matched the response schema. It does not prove that the underlying judgment was correct, that the model followed your intended policy, or that an attacker did not shape the state.
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An arXiv study dated October 4, 2026, evaluated prompt injection, adversarial suffixes, supply-chain poisoning, membership and private-attribute inference, model extraction, jailbreak and harmful-content detection, and dual-use scenarios. Its conclusion was that Jev remained vulnerable to the examined security and privacy threats. Design your application so a compromised or manipulated decision has a limited blast radius.
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Draw the trust boundaries before writing code
Use this data flow as the starting threat model:
- Untrusted source: a user, tenant, imported document, webhook, chat participant, or external tool supplies content.
- State-construction layer: your service selects fields, normalizes types, removes dangerous data, and adds trusted metadata.
- Jev API boundary: your trusted worker authenticates to
POST https://jevmodel.org/v1/systemonewith a Bearer token and, when appropriate, anIdempotency-Key. - Typed response boundary: Jev returns answers plus probability or confidence fields.
- Policy-validation layer: your code checks authorization, schema, ranges, policy version, evidence requirements, and confidence thresholds.
- Effect boundary: only this layer may enqueue a job, call a tool, change data, notify a customer, or send a case to a human.
Keep these boundaries explicit in diagrams and code ownership. A route that accepts a browser request should not have direct access to a Jev key or to privileged effectors. Put the call in a server-side service or trusted worker, and require the effect layer to receive a validated internal decision rather than raw model output.
Identify the assets and abuse cases
Record what an attacker could steal, alter, infer, or trigger. The relevant assets are:
- Jev API keys and any credential used by a worker;
- state payloads, including private customer or tenant information;
- question instructions and criteria that encode your policy;
- selected model and version identifiers;
- returned answers, confidence values, and downstream actions;
- logs, traces, request identifiers, and retained prompts;
- billing balance and rate-limit capacity; and
- privileged tools or data stores reachable after an automated decision.
For each asset, write a concrete abuse case and a consequence. Examples include a ticket that tells the evaluator to ignore your criteria, a suffix designed to flip a classification, a tenant that attempts to include another tenant’s ticket in state, a leaked key used to exhaust your quota, and a low-confidence approval that triggers an irreversible payment or account change. Rank scenarios by the damage of the resulting action, not by how impressive the model output looks.
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Construct a minimum decision state
Allow-list the fields required for the specific question. Do not serialize an entire database record or conversation when a few fields suffice. Exclude passwords, access tokens, session cookies, payment-card data, private keys, and unrelated tenant records. Attach trusted attributes—such as an authenticated tenant ID or the source system’s event ID—from your server, not from user text.
Minimization reduces privacy exposure and removes places where an injection can hide. It also makes audits reproducible: an investigator can see exactly which facts were eligible to influence a decision.
Separate instructions from data
Keep question wording and criteria in version-controlled configuration. Delimit user text as data and treat any instruction-like sentence inside it as content to classify, not as a command. Do not concatenate a user’s request into the policy text. When a workflow has allowed outcomes, describe those outcomes and their criteria explicitly rather than relying on an implied conversational rule.
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Bound size and encoding
Enforce length, nesting, character-set, and attachment limits before the API call. The current documentation lists a maximum of 1,000,000 serialized state characters, 1,800 instruction characters per question, and 2,000 serialized criteria characters per question. These limits can change, so monitor rejection rates and keep application limits below the service ceiling to leave room for metadata. Normalize Unicode, reject malformed JSON, and avoid silently truncating text that could change the meaning of a decision.
Write criteria as versioned policy
Criteria should define what each answer means, what evidence is required, and what happens when evidence is missing or contradictory. Store a policy identifier and cryptographic hash alongside the request. A policy change should produce a new version, a review record, and a test run against known cases; editing a string in production without an identifiable version makes an incident impossible to reconstruct.
Include adversarial cases in that test set:
- text that says to ignore the evaluator’s instructions;
- requests for an action the caller is not authorized to perform;
- policy statements that contradict your trusted criteria;
- attempts to smuggle tool commands or system messages in a ticket; and
- oversized or repetitive content intended to drown out relevant evidence.
Run the same cases whenever you change criteria, the selected model, state mapping, or the code that interprets an answer. A passing schema test is not a security test; assert the business outcome and the required escalation path.
Keep credentials and network access on the trusted side
The Jev documentation’s rule is direct: “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.” Load the key from a secret manager at runtime, restrict which worker identity can read it, and keep it out of URLs, exception messages, analytics events, and ordinary request logs. Rotate it on a schedule and after every suspected disclosure.
Use outbound HTTPS with certificate verification. Restrict egress from the worker to the service destinations it needs, and do not let untrusted input select an arbitrary host. If a browser or mobile client needs a Jev-backed result, have it call your own authenticated API; your server performs the Jev request and applies authorization before returning a result.
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For a suspected leak, disable the old key first, issue a replacement, search logs and source history for copies, and review billing and request activity for unauthorized use. Treat a leaked key as an active incident rather than waiting for proof of misuse.
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Validate every response before a side effect
Implement a policy gate between the Jev response and any action. The gate should:
- parse only the documented response schema and reject unknown or missing required fields;
- verify that the answer type is the one requested;
- check Choice values against an allow-list and Score values against the permitted range;
- verify that the response belongs to the expected request, tenant, policy hash, and model version;
- require the evidence fields your policy calls for, rather than accepting an unsupported conclusion;
- apply a separately configured confidence threshold; and
- confirm that the caller is authorized for the proposed effect.
Use deny-by-default or human review for actions that are irreversible, affect money, change access, delete data, or cross a tenant boundary. A low-risk label can proceed automatically only when the resulting operation is itself reversible and bounded. Never allow a model answer to choose a tool, URL, SQL statement, or shell command without an independent allow-list and argument validation.
Do not treat confidence as proof
A confidence or probability field is a signal produced with the answer, not a warranty that the answer is true. Prompt injection can produce a confident but manipulated result, and a score can be poorly calibrated for a new tenant, language, or policy version. Use confidence as one input to routing, not as the sole authorization condition.
Choose thresholds from labeled cases that represent your production traffic. Track false approvals, false denials, abstentions, and human overrides by workflow and tenant. Recalibrate or raise the review threshold when drift appears. Preserve the raw value and the threshold branch in the audit record so an investigator can distinguish “below threshold,” “schema failure,” and “policy disagreement.”
Pin versions and make decisions auditable
Pin a model version for workflows that require stable behavior. The CLI documentation warns that the jev-latest alias can move without notice and that outputs are not bit-for-bit repeatable; compare decision thresholds and business outcomes rather than exact floating-point equality.
For each evaluation, record a tamper-evident audit event containing:
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- request and correlation IDs;
- tenant and actor identifiers, using internal IDs rather than personal data where possible;
- the state schema version and a redacted or hashed representation of the input;
- question and criteria versions plus their hashes;
- the resolved model version;
- response type, answer, confidence or probability, and validation result;
- the threshold branch and selected action;
- human review, override, or appeal information; and
- latency, status code, retry count, and final disposition.
Define retention by purpose. Keep enough information to investigate a decision, but remove secrets and unnecessary personal content from traces. Restrict audit access and alert on unusual key use, sudden confidence shifts, repeated validation failures, and rate-limit exhaustion.
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Handle errors without creating duplicate actions
The documented API supports Bearer authentication and an optional Idempotency-Key. Generate a stable key for one logical operation and reuse it when retrying that operation. Do not generate a new key for every attempt, or a timeout could produce multiple evaluations that your effect layer treats as separate work.
| Condition | Response | Why |
|---|---|---|
| 429 rate limit | Retry with exponential backoff and jitter, honoring any server timing guidance; keep the same idempotency key. | The request may be valid but temporarily throttled. |
| 502 transient gateway failure | Retry a bounded number of times with backoff and the same idempotency key. | A transient upstream failure may clear. |
| 401 authentication failure | Stop retries, alert the operator, and check the secret or key status. | Repeating an invalid credential will not fix it. |
| 402 billing or balance failure | Stop automated effects and route according to your service-availability policy. | Retries do not restore an unavailable account. |
| 422 validation failure | Capture the sanitized error, fix the request or policy mapping, and send it to review if the workflow is urgent. | The payload must change before another attempt. |
| Timeout, malformed output, or low confidence | Mark the evaluation indeterminate; use human review or a safe deny path for privileged actions. | Proceeding would convert uncertainty into an authorization. |
Use a queue with a visibility timeout and a dead-letter path for repeated failures. Make the downstream effect idempotent as well: the same validated decision must not create two refunds, two account deletions, or two tool executions.
Design for Jev’s operational limits
| Documented limit | Value | Design implication |
|---|---|---|
| Questions per request | Up to 8 | Group related, independently useful questions; split larger workflows and correlate the resulting evaluations. |
| Serialized state | 1,000,000 characters | Apply a lower application cap, summarize only with a trusted process, and reject uncontrolled context growth. |
| Instruction per question | 1,800 characters | Keep policy language concise and version it rather than appending unbounded examples. |
| Criteria per question | 2,000 serialized characters | Express explicit, testable conditions; move large reference material into a separately governed decision process. |
| Rate limit | 120 requests per minute per key | Budget concurrency, queue bursts, and alert before saturation. |
| Experimental image/Box surface | Up to 4 image assets | Enforce the asset count and size before submission; treat images as untrusted input. |
These are current documented limits, not a capacity guarantee. Keep them in configuration so a service change does not require a code release, and test behavior at your own lower limits.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a hosted API or a CLI-centered workflow deliberately
A hosted API call and a workflow built around jev-cli have different boundaries. The CLI is a developer tool around the service; it does not remove the need to secure the API key or validate the resulting decision.
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|---|---|---|
| Data exposure | State crosses your network boundary to the hosted endpoint; minimize and redact it. | Local preparation can reduce what is sent, but any submitted state still crosses the API boundary. |
| Credentials | Protect a server-side Bearer key and rotate it. | The CLI redacts keys; still run it only in trusted environments and never commit its configuration. |
| Version control | Pin and log the resolved model version; rolling aliases can change. | Apply the same pinning and audit rules to CLI invocations. |
| Preflight safety | Implement schema and policy checks in your service. | Use the CLI’s offline validate and --dry-run checks before sending. |
| File and MCP access | Enforce your own allow-lists and tenant isolation. | Keep the CLI’s MCP file boundary deny-by-default and approve only required paths. |
| Supply chain | Review SDK and dependency updates. | Verify signed updates and checksums; review release and dependency changes. |
| Observability | Centralize request IDs, policy hashes, retries, and outcomes. | Forward equivalent audit fields from local runs; do not rely on terminal history. |
| Availability and cost | Design around the documented rate limit, transient errors, latency, and account balance. | Offline validation avoids needless calls but does not provide offline Jev decisions. |
The right choice depends on your data-residency requirements, network boundary, operator model, and the consequence of an incorrect action. Neither option makes an automated decision safe without the policy gate.
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Secure the client and supply chain
Run local validation before deployment and use dry runs for changes that could alter state mapping or criteria. Keep file access deny-by-default when the CLI is used with MCP. Verify signed updates and checksums, pin dependency versions where practical, and review release notes for permission or network changes. Require HTTPS certificate verification and ensure redirects are not followed into an unapproved destination. The CLI documentation describes contact limited to the TypeSafe API and GitHub Releases, no telemetry, key redaction, and no redirect following; preserve those properties in wrappers and automation rather than assuming a wrapper inherits them.
Build the CLI or SDK in a controlled pipeline, scan dependencies, and separate the identity allowed to validate a policy from the identity allowed to execute a privileged action. A developer workstation should not be able to approve production effects merely because it can call Jev.
Run hostile-input and failure drills
Before enabling automation, exercise the complete path with test tenants and non-destructive effects:
- Submit ordinary cases and verify each answer type, range, threshold branch, and audit field.
- Inject “ignore the criteria” language, contradictory instructions, tool-shaped text, and adversarial suffixes into every user-controlled field.
- Try cross-tenant identifiers, missing evidence, malformed JSON, Unicode edge cases, and maximum-size state.
- Force 429, 502, timeout, 401, 402, and 422 handling in a staging environment.
- Replay a request with the same idempotency key and confirm that no duplicate effect occurs.
- Change the model alias or policy hash and verify that the validator rejects an unexpected version.
- Confirm that low confidence and malformed output reach review or denial rather than the effect queue.
- Revoke a test key and practice replacement, log review, and incident notification.
Measure outcomes, not just API success: unauthorized approvals, missed escalations, review latency, duplicate effects, and the percentage of decisions with complete provenance.
Production checklist
- Threat model documents the state, API, validation, and effect boundaries.
- Only a trusted server or worker can read the Jev key.
- State is allow-listed, tenant-scoped, redacted, size-bounded, and retained deliberately.
- Questions and criteria have owners, versions, hashes, and adversarial regression cases.
- Responses are checked for type, enum or range, provenance, authorization, evidence, and confidence policy.
- Privileged effects require human approval or an explicit fail-closed rule.
- Model version, policy hash, request ID, threshold branch, retries, and overrides are auditable.
- 429 and 502 retries use bounded exponential backoff and idempotency; permanent errors stop.
- CLI validation, dry runs, signed updates, checksums, HTTPS verification, and MCP path restrictions are enforced.
- Drills cover injection, key compromise, service failure, replay, and model or policy changes.
What a safe default looks like
For a low-impact classification, send a minimized, tenant-scoped state from a server worker, ask a small set of explicit Choice or Score questions, validate the response, and queue a reversible action. For an access change, deletion, financial operation, or external tool call, require stronger evidence and human review; a timeout, malformed answer, unexpected version, or low confidence should deny or pause the operation. This posture accepts that Jev can be useful for structured decisions while treating user input, model output, and confidence as untrusted until your controls make them safe enough for the specific effect.
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