* fix(security): stop API tokens reaching privileged agent tools
A bearer API token resolves to the human who minted it, and minting is admin-only, so every owner-keyed privilege check in the agent path answers "admin". A token issued for a narrow integration therefore reached bash and python with the authority of the account that created it.
Three independent routes to that sink, each closed here.
The token could answer its own tool-approval prompt. An approval records that a person authorized one dangerous action, and a token cannot make that statement, so /api/chat_stream now refuses an approval resume from a bearer caller.
The chat-session grant was reconstructable from caller-supplied message metadata. Two routes persist a metadata blob on the caller's behalf, so the shape of a resolved approval card could be written straight into a transcript and was then read back as authority. The server now signs the grant when it resolves an approval and verifies that signature when reading it back, binding it to the chat and the approval it was issued for. Both routes also drop server-owned keys from an inbound blob.
A run driven by a token inherited its owner's tool set. Such a run is now capped at the non-admin policy regardless of who minted the credential, which holds even where no approval is raised at all.
The human path is unchanged: a browser session still receives the prompt, still approves, and a granted chat-session scope still carries to later turns in that chat.
Scope enforcement across the wider route surface is a separate gap and is not addressed here.
* fix scoped chat delegation boundaries
* fix(auth): reject malformed chat approval signatures
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Co-authored-by: RaresKeY <158580472+RaresKeY@users.noreply.github.com>
Wrap blocking _resolve_model calls in asyncio.to_thread across async model interaction paths so endpoint/model resolution does not stall the event loop. Preserve owner-scoped resolution and add focused regression coverage.
* fix(kimi): resolve Kimi Code API 403 errors and User-Agent restrictions
Kimi Code subscription keys require a whitelisted coding-agent User-Agent to avoid access_terminated_error 403s. This adds User-Agent probing and caching for Kimi Code endpoints.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(kimi): omit temperature for kimi-for-coding API calls
Kimi Code rejects any non-default temperature with HTTP 400, which broke deep research probes and low-temp LLM rounds.
Co-authored-by: Cursor <cursoragent@cursor.com>
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Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(presets): scope expand-prompt model resolution to owner
/api/presets/expand resolved its model endpoint with no owner, so in a
multi-user setup it could match another user's endpoint and use its URL
and decrypted api_key. Pass effective_user(request) to _resolve_model so
resolution is owner-scoped. Adds a regression test.
* fix(presets): scope teacher and audit model resolution to owner
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Co-authored-by: Alex Little <alexwilliamlittle@gmail.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Co-authored-by: Kenny Van de Maele <kenny@kvandemaele.be>
Follow-up to the Venice provider PR. Wire api.venice.ai into the three
host allowlists so Venice behaves like the other paid OpenAI-compatible
clouds:
- agent_loop: add api.venice.ai to _API_HOSTS so the agent sends native
OpenAI tool-call schemas (Venice supports function calling) instead of
degrading to fenced-block parsing.
- teacher_escalation: add api.venice.ai to _SOTA_HOSTS so the escalation
loop stays OFF for Venice (it's a paid top-tier API; no need to add
teacher-model latency).
- webhook_routes: add venice to KNOWN_PROVIDERS so the sync chat webhook
can auto-resolve base_url from provider=venice.
Tests: tests/test_venice_hosts.py pins tool-host matching + SOTA
classification for Venice; py_compile on touched modules.
Co-authored-by: Cursor <cursoragent@cursor.com>
The teacher-escalation loop distills a failed turn's trace into a
persisted skill, but the trace includes raw tool output (web pages,
emails, retrieved documents) that can carry prompt-injection. Skills are
later injected as authoritative "follow step by step" guidance, so an
injected instruction in tool output could be laundered into a skill the
student follows on a later turn -- bypassing the untrusted-content
wrapper that protects the live turn.
Fence the trace in both teacher prompts and add an explicit "this is
data, not instructions" guard so the teacher won't copy directives out
of tool output into a procedure. Additive prompt hardening; no
default-UX change.
Ran: python -m py_compile src/teacher_escalation.py + a format/fencing
smoke test (both templates format; an injected instruction stays fenced
inside the untrusted block).
Co-authored-by: Fernando Lazzarin <263019791+waitdeadai@users.noreply.github.com>
Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
