Two related issues with how the proxy handles client-supplied
``api_base`` / ``base_url`` overrides on chat-completion requests:
1. **SSRF gate bypass** — ``check_complete_credentials()`` returned
``True`` for any non-empty ``api_key``, allowing the
``is_request_body_safe`` ``banned_params`` loop to admit ``api_base``
/ ``base_url`` values that point at private (RFC 1918), loopback,
link-local, or cloud-metadata addresses. Now: when the gate sees a
client-supplied ``api_base`` / ``base_url``, it runs the URL through
``litellm_core_utils.url_utils.validate_url`` (DNS-resolves, blocks
internal/IMDS/LL networks, defends against rebinding). Rejection
raises with a clear message.
2. **Admin-config leak on base override** —
``get_dynamic_litellm_params`` only carried the three clientside keys
(``api_key``, ``api_base``, ``base_url``) from request to upstream
call. Other admin-configured fields on ``litellm_params`` —
``organization``, ``extra_body``, ``extra_headers``, ``api_version``,
``azure_ad_token``, AWS / Vertex creds, etc. — flowed through
unchanged. With base redirected to a client-controlled server, those
admin secrets were sent to the attacker. Now: when ``api_base`` /
``base_url`` is in ``request_kwargs``, drop those admin-config
fields from ``litellm_params`` unless the caller re-supplied them.
Tests cover the SSRF-target rejection per URL field, the admin-secret
clearing on base override, the don't-clear case when only ``api_key``
is overridden (BYOK pattern), and the don't-overwrite case when the
caller resupplies fields like ``organization`` themselves.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(vertex passthrough): log :embedContent and :batchEmbedContents responses
* test(vertex passthrough): add unit tests for :embedContent and :batchEmbedContents logging
* fix(vertex passthrough): extract input text from request body for embedContent token counting
* fix(vertex passthrough): add embedContent and batchEmbedContents to TRACKED_VERTEX_ROUTES
* fix(vertex passthrough): detect Google AI Studio URLs in embedContent handler
* test(vertex passthrough): add unit test for Google AI Studio URL embedContent provider detection
* style: black format vertex_passthrough_logging_handler
Extract the admin team-header attachment into a helper so
auth_builder stays under the 50-statement lint threshold; apply
black formatting to the two files flagged on the prior commit.
No behavior change.
Scope the header-driven team fetch to LLM API routes so admin
management routes keep the pre-existing bypass behavior (no
phantom teams, no 404s on mgmt calls). Team context is threaded
onto UserAPIKeyAuth so spend logs, rate limits, and team_models
attribution are correctly applied when admins act on behalf of
a team via x-litellm-team-id.
* fix(proxy): honor object_permission for managed vector store access
* perf(proxy): preload team object_permission on UserAPIKeyAuth
Populate team_object_permission during virtual-key and JWT auth when the
team is loaded, so can_user_access_vector_store uses it in memory first
and only falls back to get_object_permission by id when missing.
Made-with: Cursor
* fix(team_endpoints): auto-add SSO team members to org for proxy admins
* test: proxy_admin vs team_admin security boundary for team→org move
* screenshots: before/after for team-org SSO fix
* fix(team_endpoints): restore staging security features dropped in SSO commit
Co-Authored-By: Ishaan Jaff <ishaan@berri.ai>
* style: black formatting for team_endpoints
MCP server CRUD endpoints (/v1/mcp/server*) were bundled with MCP
tool-call / passthrough endpoints under llm_api_routes, so setting
DISABLE_LLM_API_ENDPOINTS=true on admin-only nodes also blocked the
Admin UI from listing, adding, or attaching MCP servers.
Separate mcp_inference_routes (data-plane, gated by
DISABLE_LLM_API_ENDPOINTS) from mcp_management_routes (control-plane,
gated by DISABLE_ADMIN_ENDPOINTS). Keep mcp_routes as a union for
backward compat with allowed_routes=["mcp_routes"] virtual key configs.
Upgrade is_management_route to pattern-aware matching so
/v1/mcp/server/{path:path} resolves for concrete IDs.
Temporary MCP OAuth sessions were kept in process-local memory, so on
multi-instance/LB proxy deployments a session created on instance A could
not be found when the follow-up /server/oauth/{server_id}/... request
landed on instance B.
Persist temporary session records to Redis (encrypted with the existing
proxy encryption helpers) as a best-effort L2 cache alongside the current
in-memory L1. Convert get_cached_temporary_mcp_server to async and await
it from the authorize/token/register OAuth endpoints.
Made-with: Cursor
Vertex multi-region endpoints (e.g. us, eu) use the rep host pattern, not
{geo}-aiplatform.googleapis.com. Regional IDs still contain a hyphen.
common_utils.get_vertex_base_url centralizes the rule for SDK/API URL building.
Proxy pass-through duplicates the same branching in a local get_vertex_base_url
(with trailing slashes) to avoid importing from common_utils there; live
WebSocket passthrough uses the same multi-region host logic for wss://.
Tests cover us/eu for the common_utils helper.
Made-with: Cursor
Sibling tests were mutating litellm.proxy.proxy_server.master_key and
prisma_client with raw setattr. Values leaked across tests in the same
xdist worker, flipping the auth short-circuit in user_api_key_auth and
causing unrelated tests (e.g. test_ui_view_session_spend_logs_pagination)
to return 401 instead of 200.
Replace raw setattr with monkeypatch in the two offending files and add
an autouse conftest fixture that snapshots/restores the known-leaky
module globals for every proxy test.
Two fixes to proxy-db CI:
1. test_realtime_webrtc_endpoints.py's `proxy_app` fixture mutated the
module-global `proxy_server.master_key` without restoring it, leaking
state into any test that shared the same xdist worker. Under
--dist=loadscope with 2 workers (GHA proxy-endpoints), this caused the
google_endpoints tests to fail with "No api key passed in." because
user_api_key_auth saw a set master_key and a missing API key on the
test request. The fixture now saves and restores the original value.
2. Address the Greptile note that the semantic shard design has no
catch-all, so a new test file added to tests/proxy_unit_tests/ without
a matrix entry would silently skip CI. Adds an assert-shard-coverage
job that enumerates test_*.py files and fails the workflow if any are
not referenced by a matrix entry, with a clear message telling the
author which semantic shard to place it in. All proxy-db shards now
depend on this guard.
The mocked async_increment_cache_pipeline is invoked from Router's
deployment_callback_on_success, registered as an async success callback.
Those callbacks are enqueued to GLOBAL_LOGGING_WORKER and run on a
background task, so the mock may not have been called yet when the test
asserts on it. Flush the worker before asserting.
Two independent deflakes:
1. test_ui_view_spend_logs_unauthorized (unit) was returning 400 instead
of 401/403 when earlier tests in the file left proxy-auth globals
(prisma_client, master_key, user_custom_auth, general_settings,
user_api_key_cache) in a state that let invalid tokens pass auth and
fall through to the endpoint's own start_date/end_date validation.
Add an autouse fixture that pins those globals to their import-time
defaults for every test in the file. Harden the assertion to include
response body so future flakes are diagnosable.
2. test_basic_spend_accuracy (CI job proxy_spend_accuracy_tests) depends
on the Redis transaction buffer flushing spend to Postgres. The buffer
uses a single global pod-lock key (cronjob_lock:db_spend_update_job)
and a single global buffer list key. Pointing the proxy at the shared
remote Redis means concurrent CI pipelines contend for the same lock
and can drain each other's buffer into the wrong database. Add a
start_redis reusable command that boots a per-job redis:7-alpine
container (digest-pinned), and switch proxy_spend_accuracy_tests to
REDIS_HOST=host.docker.internal:6379 so lock and buffer state are
isolated per CI run.
- Add gpt-5.5 to GPT5_MODELS parametrized list so both OpenAIGPT5Config
and AzureOpenAIGPT5Config routing tests cover the new model.
- Add test_generic_cost_per_token_gpt55 verifying the new entry's
cost-map values ($5/$0.50/$30 per 1M) and that generic_cost_per_token
returns the expected prompt/completion costs.
* feat: add gpt-5.5 to model cost map
Add gpt-5.5 entry with pricing from OpenAI flagship page:
input $5/1M, cached input $0.50/1M, output $30/1M, 272K context.
* test: add gpt-5.5 coverage for model cost map and gpt-5 routing
- Add gpt-5.5 to GPT5_MODELS parametrized list so both OpenAIGPT5Config
and AzureOpenAIGPT5Config routing tests cover the new model.
- Add test_generic_cost_per_token_gpt55 verifying the new entry's
cost-map values ($5/$0.50/$30 per 1M) and that generic_cost_per_token
returns the expected prompt/completion costs.
The periodic budget-window reset job filtered keys/teams with
`where={"budget_limits": {"not": None}}`. The prisma-client-python
library does not support null-filtering on `Json?` columns (no
DbNull/JsonNull sentinel — upstream issue #714). The client drops the
`None` value during serialization and the engine rejects the query with
`MissingRequiredValueError: where.budget_limits.not: A value is
required but not set`, so neither the key nor team reset path runs.
Switch those two `find_many` calls to `query_raw` with
`WHERE budget_limits IS NOT NULL`, selecting only the PK and the
`budget_limits` column. Writes still go through the ORM. Add unit tests
covering the expired/unexpired paths for keys and teams, string-encoded
JSON payloads, empty payloads, error isolation between the two paths,
and a regression guard asserting the query still uses `IS NOT NULL`.
Align the Ruby, Node.js, and npm install path with the rest of the
config. Three separate upstream installers were being invoked via
\`curl ... | bash\` or unlocked \`npm install\`:
- RVM's \`get.rvm.io/stable\` installer (mutable upstream script).
Replace with a shallow git clone of the rvm/rvm repo at tag 1.29.12
and verify HEAD matches the published commit SHA before running the
local \`./install\` script. Same pattern already used for the
helm-unittest plugin in .github/workflows/helm_unit_test.yml.
- NodeSource's \`deb.nodesource.com/setup_18.x\` piped into sudo bash.
Replace with a direct download of the Node.js 18.20.8 linux-x64
tarball from nodejs.org, verified against the published
SHASUMS256.txt digest before extraction.
- \`npm install @google-cloud/vertexai @google/generative-ai\` and
\`--save-dev jest\` resolved fresh from the npm registry on every
run. Add \`tests/pass_through_tests/package.json\` with pinned
direct-dep versions and commit the generated package-lock.json, then
switch CI to \`npm ci\` (exact lockfile install, fails on drift).
Also scopes the Ruby+JS test runners to \`tests/pass_through_tests/\`
so they pick up the committed package.json rather than writing
node_modules at repo root.
Unit Tests: Proxy DB Operations / proxy-db (auth-checks, tests/proxy_unit_tests/test_auth_checks.py tests/proxy_unit_tests/test_user_api_key_auth.py, 20, 8) (push) Has been cancelled
Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py --ignore=tests/proxy_unit_tests/test_p… (push) Has been cancelled
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Unit Tests: Proxy DB Operations / proxy-db (remaining, tests/proxy_unit_tests --ignore=tests/proxy_unit_tests/test_key_generate_prisma.py --ignore=tests/proxy_unit_tests/test_auth_checks.py --ignore=tests/proxy_unit_tests/test_user_api_key_auth.py --ignore=tests/proxy_unit_tests/test_p… (push) Has been cancelled
The original check `"gpt-5-chat" not in model` already correctly
classifies all current gpt-5 variants (including gpt-5.3-chat and
gpt-5.1-chat, which do NOT contain the substring "gpt-5-chat"). This
change replaces it with an explicit `startswith("gpt-5-chat")` prefix
test on the provider-prefix-stripped model name.
The new check is functionally equivalent for all existing model names
but makes the classification boundary unambiguous and forward-safe:
future model names that might contain "gpt-5-chat" as an interior
substring won't accidentally be excluded from the GPT-5 reasoning path.
Also moves the new regression test from tests/ root to
tests/test_litellm/llms/openai/ so it is included in `make test-unit`.
* fix(anthropic): handle tool_choice type 'none' in messages API
* test(anthropic): add regression test for tool_choice type 'none'
---------
Co-authored-by: BillionClaw <267901332+BillionClaw@users.noreply.github.com>
Co-authored-by: Krrish Dholakia <krrish+github@berri.ai>
When reasoning_auto_summary is enabled (via litellm_settings or env var),
automatically set thinking.display="summarized" on native /v1/messages
requests. This ensures thinking content is returned in the response
instead of being omitted (the default on Claude 4.7+).
Only applies when thinking is enabled (type != "disabled").
The existing reasoning_auto_summary flag already handles the
/v1/responses path (summary="detailed") and the chat/completions
adapter path — this extends coverage to the native messages handler.
