* feat(mcp): scope a key to zero MCP servers with no-mcp-servers sentinel
A key under a team that has MCP servers had no way to opt out of them;
an empty list has always meant "inherit the team". This adds a
no-mcp-servers sentinel (mirroring no-default-models for models) so a key
can declare an explicit zero that overrides team inheritance, additive
grants, and allow_all_keys servers, surfaced as an exclusive "No MCP
Servers" option in the key create/edit UI.
* refactor(ui): centralize no-mcp-servers sentinel in a shared constant
The sentinel string was defined under two different local names and
inlined in two more files; a single exported constant removes the drift
risk flagged in review.
* fix(mcp): enforce no-mcp-servers sentinel on toolset-scoped routes
Toolset scoping replaced a key's mcp_servers with the toolset's servers,
dropping the no-mcp-servers sentinel, so a key opted out of all MCP could
still execute a granted toolset's tools via /toolset/{name}/mcp. Deny
toolset access when the key carries the sentinel, checked before the admin
branch to match get_allowed_mcp_servers.
(cherry picked from commit 19a29e0579)
* fix(guardrails): return 400 not 500 when AIM blocks a request
AIM guardrail blocks raised a bare HTTPException whose type and param
serialized as the literal string "None", which broke OpenAI-SDK error
parsing for downstream consumers. Switching AIM to raise a ProxyException
surfaced a second bug: the shared error funnel re-derived the HTTP status
from a nonexistent status_code attribute and downgraded the 400 to a 500.
The funnel now honors an already-normalized ProxyException rather than
rebuilding it, and ProxyException is excluded from llm_exceptions alerting
so a content-policy block no longer pages on-call as an LLM API failure
Resolves LIT-3751
* fix(guardrails): route all AIM rejection paths through ProxyException
The block-action fix left two AIM rejection paths raising a bare
HTTPException: the multimodal anonymize rejection and the output-side
block. Both serialized type and param as the literal string "None", the
same malformed shape the block fix removed. Funnel all three through a
shared _rejection helper so they return a conformant OpenAI error body.
The output block carries content_policy_violation; the multimodal
rejection stays a plain invalid_request_error because it is a usage
error, not a policy violation
Resolves LIT-3751
* fix(guardrails): record AIM ProxyException blocks in failure logs
Switching AIM blocks from HTTPException to ProxyException made
_is_proxy_only_llm_api_error return False for them, so
_handle_logging_proxy_only_error was skipped and the blocked prompt was
dropped from the configured failure loggers. Classify ProxyException as a
proxy-only error alongside HTTPException so guardrail blocks are recorded
again, matching the prior behavior. The llm_exceptions alert suppression
is a separate check and stays in place
Resolves LIT-3751
* style(guardrails): use str | None over Optional[str] in AIM _rejection
* style(guardrails): collapse AIM _rejection signature per black
(cherry picked from commit b5fcd859be)
* fix(guardrails): stop re-initializing DB guardrails on every poll
InMemoryGuardrailHandler._has_guardrail_params_changed compared the
in-memory LitellmParams against the raw dict loaded from the DB. The
in-memory side carries every field default and coerces enums via
model_dump(), while the DB side only holds the keys originally stored,
so the two shapes never compared equal and the guardrail was rebuilt on
every poll cycle.
Each rebuild created a fresh instance, but delete_in_memory_guardrail
only removed the old callback from litellm.callbacks. Request handling
promotes guardrail callbacks into the success/failure/async lists, so
the previous instance stayed referenced there and instances accumulated.
Normalize both sides through LitellmParams(...).model_dump() before
diffing, and purge the callback from every callback list on delete.
* refactor(guardrails): narrow params-normalization fallback to ValidationError
The comparison normalizer caught a bare Exception and silently fell back
to the raw dict, which hid the cause and quietly degraded the affected
guardrail back to re-initializing on every poll. Catch only the
ValidationError that LitellmParams construction can raise, log a warning
so the offending row is diagnosable, and let any other error surface
instead of being swallowed.
* refactor(callbacks): add remove_callback_from_all_lists helper to manager
Move the knowledge of which callback lists a callback can be promoted
into out of the guardrail registry and into LoggingCallbackManager, where
the rest of the callback-list bookkeeping already lives. delete_in_memory_guardrail
now delegates to the new helper instead of iterating the lists itself.
(cherry picked from commit 9fa74ad8b4)
* fix(guardrails): run pre_call hook once for model-level guardrails
A CustomGuardrail attached to a deployment via litellm_params.guardrails
gets its async_pre_call_hook invoked twice per request: once by the proxy
pre-call loop and again by async_pre_call_deployment_hook after the router
spreads the model-level guardrails into the top-level request kwargs.
Record in request metadata that the proxy pre-call loop already ran a given
guardrail, and have the deployment hook skip it when the marker is present.
Direct-SDK usage never runs the proxy loop, so the deployment hook stays the
sole invocation there and still fires exactly once.
The marker key is stripped from untrusted caller metadata so a request body
cannot suppress a model-only guardrail by pre-seeding it.
* fix(guardrails): mark pre_call dedup on the post-hook request data
Record the exactly-once marker after async_pre_call_hook runs, on the data
object that flows downstream, rather than before it. A guardrail whose hook
returns a brand-new request dict (instead of mutating or spreading the one it
received) would otherwise discard the marker, letting the deployment hook
re-run the guardrail a second time.
(cherry picked from commit 4faeabc254)
* fix(integrations): cap Anthropic cache_control injection at 4 blocks
Respect Anthropic's 4 cache_control breakpoint limit by counting client-supplied blocks, skipping messages that already carry cache_control, and stopping further auto-injection once the limit is reached.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(integrations): reserve cache slot for tool_config and short-circuit cap
Address review feedback on the cache_control cap: break out of the injection loop before resolving target indices once the limit is reached, and reserve one of the four breakpoint slots when a tool_config injection point is present so the cachePoint appended by the Bedrock transform does not push the total past Anthropic's limit.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
(cherry picked from commit fc9d789d24)
* fix duplicate cost callbacks for anthropic streaming pass-through
Two bugs caused _PROXY_track_cost_callback to see stream=True +
complete_streaming_response=None on every streaming pass-through request,
making the dedup guard in dispatch_success_handlers permanently inactive:
1. pass_through_endpoints.py created the Logging object with stream=False
for all requests. _is_assembled_stream_success short-circuits on
self.stream is not True, so has_dispatched_final_stream_success was
never set and any second dispatch went through unchecked.
Fix: set logging_obj.stream = True after stream detection.
2. _create_anthropic_response_logging_payload set complete_streaming_response
inside the try block after litellm.completion_cost(), so a pricing error
caused an early return without setting it on model_call_details.
Fix: set complete_streaming_response before the try block.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* fix stream
* add stream to logging obj
* test(pass_through): give mock logging object a real model_call_details dict
The anthropic passthrough logging payload now records the assembled
response on model_call_details before cost calculation, which requires
model_call_details to support item assignment. In production it is always
a dict; the existing unit test stubbed the logging object with a bare Mock
whose attribute is not subscriptable, so the new assignment raised
TypeError. Use a real dict to match the production logging object.
* test(pass_through): cover streaming logging-obj stream flag
The streaming branch of pass_through_request that marks the logging object
as streaming (logging_obj.stream and model_call_details["stream"]) had no
unit coverage, so the patch coverage gate flagged it. Add a regression test
that drives a streaming pass-through request through pass_through_request and
asserts the logging object is flagged as a stream before dispatch.
* test(pass_through): cover SSE-response stream flag fallback branch
The auto-detected streaming branch of pass_through_request (when a request
that was not flagged as streaming returns a text/event-stream response) sets
logging_obj.stream and model_call_details["stream"] but had no unit coverage,
so the codecov patch gate failed at 60%. Drive a non-streaming pass-through
request whose upstream response is SSE through pass_through_request and assert
the logging object is flagged as a stream before dispatch.
* fix(pass_through): gate complete_streaming_response on stream flag
perform_redaction only scrubs complete_streaming_response when
model_call_details["stream"] is True. Setting it unconditionally for
non-streaming Anthropic pass-through responses left the assembled
response unredacted in model_call_details, which is handed to logging
callbacks as kwargs when message logging is disabled. Only record it for
actual streaming responses so redaction always applies.
---------
Co-authored-by: mubashir1osmani <mubashir.osmani777@gmail.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
(cherry picked from commit 2bbdbfa5c3)
* fix: stop use_chat_completions_api flag from leaking into provider request body
use_chat_completions_api is a LiteLLM control flag that forces the
/responses -> /chat/completions bridge. It was missing from
all_litellm_params, so get_non_default_completion_params treated it as a
model-specific param and forwarded it to the upstream provider. A
model-level "use_chat_completions_api: true" in the proxy config therefore
reached the chat-completions path and was rejected by strict providers
(OpenAI/Anthropic) with HTTP 400 for an unknown body field.
Register it as a known internal param so it is stripped on every path
(completion, the responses bridge that calls litellm.completion, and
filter_out_litellm_params).
Adds a regression test driving litellm.completion() with a mocked OpenAI
client that asserts the flag never reaches the request body.
* test: clarify extra_body assertion in use_chat_completions_api leak test
Replace the misleading 'not in ... or {}' precedence idiom with an explicit
parenthesized guard that also handles extra_body being None.
(cherry picked from commit 65b6e04da6)
Backport of #30220 dependency bumps adapted to this line: pypdf pinned to
6.13.1 (this line exact-pins these dependencies), tornado and aiohttp held via
[tool.uv] constraint-dependencies (tornado>=6.5.6; aiohttp>=3.13.5,<3.14 so a
lock regeneration cannot move onto 3.14 while vcrpy is incompatible), and the
dashboard vitest devDependencies raised 3.2.4 -> 3.2.6. Locks refreshed in a
follow-up commit.
The grace-period branch assigned the recursive get_data result (a
finished LiteLLM_VerificationTokenView) back into the variable that the
combined-view dict normalization then subscripts, raising TypeError on
every request made with a rotated key inside its grace window; auth
surfaced that as a 401. Return the recursive result directly instead.
Regression test drives the full get_data flow: old hash misses the view,
deprecated table resolves to the active token, and the call must return
the view object
(cherry picked from commit 5047eaf7f0)
Targeted subset of staging commit cfcdf8714a (#30202): only the
anthropic_passthrough_logging_handler.py hardening hunks and their four
tests are taken; the rest of that staging batch is intentionally excluded.
(cherry picked from commit cfcdf8714a)
(cherry picked from commit 973c7eb8d6)
Applied as the squash diff of PR #29788 (head 9800b2f17c), which landed
upstream inside the litellm_oss_staging_080626 sync (32c88ca74f, #29932)
and has no standalone commit to cherry-pick.
Backport prerequisite for #29788. Applied as the squash diff of PR #29490
(head 50ab150fa6^..), which landed upstream inside the litellm_oss_staging_040626
sync (cb041966bf, #29671) and has no standalone commit to cherry-pick.
Capture user_id and extra_info from metadata or litellm_metadata. The single-bag read dropped identity whenever a request carried a present litellm_metadata field (null or a user-supplied dict), since /chat/completions routes the authenticated identity into metadata while the guardrail read litellm_metadata first
(cherry picked from commit 1bbaf1c39d)
* fix(proxy): authorize batch files using upload target_model_names (LIT-3593)
After replace_model_in_jsonl, body.model is a stripped provider id. Reverse-mapping it via resolve_model_name_from_model_id is first-match on model_list and caused false 403s when multiple deployments share the same stripped name. Use target_model_names from the unified file id instead.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(proxy): restore resolve_model_name_from_model_id for JSONL fallback path (LIT-3593)
Restores the reverse-lookup for the JSONL body.model fallback path so that
legacy/pre-target_model_names managed files still map stripped provider IDs
back to proxy aliases before auth. Also cleans up redundant `or None`.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
* Revert "fix(proxy): restore resolve_model_name_from_model_id for JSONL fallback path (LIT-3593)"
This reverts commit 30d2e96f77.
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>
(cherry picked from commit 2cd7e87485)
* Add Claude Fable 5 across Anthropic, Bedrock, Vertex AI, and Azure AI
Adds cost map entries for claude-fable-5 ($10/$50 per MTok, 1M context,
128K output, adaptive thinking only) on the Anthropic API, Bedrock
converse (base, global, and us/eu geo inference profiles at the 10%
regional premium), Vertex AI, and Azure AI (Microsoft Foundry, which
serves Fable 5 with the full 1M context window unlike Opus 4.8).
Registers anthropic.claude-fable-5 in BEDROCK_CONVERSE_MODELS, lists the
model in the setup wizard, and extends the reasoning effort e2e grid.
The Bedrock, Vertex, and Azure grid cells carry fail_reason markers
until the CI accounts are provisioned: Bedrock needs the provider data
sharing opt-in Fable 5 requires, and the Foundry resource needs a
claude-fable-5 deployment.
The first-party entry carries provider_specific_entry {us: 1.1} for the
inference_geo premium and deliberately no fast multiplier since Fable 5
has no fast mode.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Drop removed sampling params for Claude 4.7+ when drop_params is set
Fable 5, Opus 4.7, and Opus 4.8 removed sampling params: the API rejects
top_p, top_k, and any temperature other than 1 with a 400. LiteLLM was
forwarding them even with drop_params enabled because the Anthropic and
Bedrock converse transformations passed temperature/top_p through
unconditionally.
Mirror the GPT-5/o-series handling: temperature=1 still passes through,
other values and any top_p are dropped when drop_params is set, and
without drop_params a clean client-side UnsupportedParamsError tells the
caller how to opt in, instead of surfacing the raw provider error.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Drive sampling param gating from the cost map and cover top_k
Greptile review follow-ups on the sampling param fix: the restriction for
Fable 5 / Opus 4.7 / 4.8 is now declared as supports_sampling_params: false
on every affected cost map entry (perplexity excluded; that route is
OpenAI-compatible and maps sampling params upstream) and read back through
a tri-state map lookup, keeping the name check only as a fallback for
provider-routed ids whose hosted map entries predate the flag, the same
layering supports_adaptive_thinking uses. top_k bypasses map_openai_params
as a provider-specific kwarg, so it is gated at the shared
AnthropicConfig.transform_request boundary (direct, Bedrock invoke, Vertex,
Azure) and in the Bedrock converse _handle_top_k_value path, with
drop_params threaded through the converse transform helpers.
Also updates the reasoning effort grid cell count assertion for the four
Fable 5 rows added on this branch (29 x 11 cells).
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* Declare supports_sampling_params in the cost map schema
The model map validation schema uses additionalProperties: false, so the
new flag must be declared for the 28 entries that carry it; this was the
one failing job (misc / Run tests) on the previous commit.
https://claude.ai/code/session_01MZarYYT3aS7DxaNjoax6Gm
* fix(bedrock): gate top_k=0 on converse to match Anthropic boundary
Truthiness check let top_k=0 silently disappear on models that removed
sampling params, while AnthropicConfig.transform_request treats 0 as
present and raises UnsupportedParamsError (or drops when drop_params is
set). Switch to 'is not None' so converse, direct Anthropic, invoke,
Vertex, and Azure all behave the same for top_k=0.
---------
Co-authored-by: Cursor Agent <cursoragent@cursor.com>
The latest 1.87.x GitHub release is v1.87.0, so the branch should sit one patch
ahead at 1.87.1 as the patch-to-be. The GHSA-q775 backport (#29636) additionally
bumped 1.87.1 -> 1.87.2, cutting a version ahead of release. This reverts just the
version bump and its uv.lock refresh, keeping the backported fix and hardening
Capture _requested_team_id before the default_key_generate_params loop runs and
key the UI/CLI session-token budget-ceiling exemption off it, instead of the
post-defaults data.team_id. On an install that sets
default_key_generate_params.team_id, a session token requesting a personal key
(no explicit team_id) would otherwise have data.team_id auto-filled, flipping
is_ui_session_team_key on and bypassing the delegated-authority ceiling -- the
exact escalation GHSA-q775 closed. Mirrors the existing pre-defaults capture of
_requested_max_budget. Adds a regression test.
https://claude.ai/code/session_01RT583b1khYC3wjLrQ5hT5h
Non-admin users creating a team key through the UI were rejected with
"max_budget cannot exceed the caller's own max_budget (0.25)". The request is
authenticated by a UI/CLI session token whose max_budget is the per-session chat
spend cap (max_ui_session_budget, default $0.25), and the delegated-authority
budget ceiling (GHSA-q775-qw9r-2r4g) treated that cap as a delegation limit.
Skip the ceiling only when a session token creates a team key (data.team_id set);
that key's spend is bounded by the team budget at request time. Personal keys and
every other non-admin caller keep the ceiling, so a session token cannot mint an
arbitrary-budget personal key.
(cherry picked from commit 97ba7e1a30)
* fix(vertex): strip output_config.effort for models that reject it
Haiku 4.5 on Vertex AI does not support output_config.effort and 400s with
"output_config.effort: Extra inputs are not permitted". PR #27074 emptied
VERTEX_UNSUPPORTED_OUTPUT_CONFIG_KEYS so effort would forward for Opus/Sonnet
4.6+, but that made the strip unconditional across every Vertex Anthropic
model, including ones that don't support it. Claude Code injects effort into
its default Messages payload, so `claude --model claude-haiku-4.5` started
failing.
Make the sanitizer model-aware: drop output_config.effort for models that
don't advertise output_config support (or any reasoning effort level) while
forwarding it for those that do. The fix covers both the chat-completion and
Messages pass-through transformation paths since they share the helper.
* chore(vertex): log at debug when dropping unsupported output_config.effort
Operators pointing an unregistered Vertex Claude alias that does support
effort would otherwise see it stripped with no signal. Debug level keeps it
out of normal logs since Claude Code sends effort on every request.
(cherry picked from commit cc55662e5f)
* fix(key_generate): allow team members to create keys on org-scoped teams
When a virtual key is created for a team, enterprise logic inherits the
team's organization_id onto the key (add_team_organization_id). Since the
VERIA-55 org-IDOR fix, /key/generate then required the caller to be an
explicit LiteLLM_OrganizationMembership member of that org, returning
403 "Caller is not a member of organization_id=<uuid>". Admins normally
only add users to teams (not orgs), so self-serve key creation regressed
for any user on an org-scoped team (regression since v1.84.0-rc.1).
Skip the org-membership check when organization_id was inherited from the
key's team (organization_id == team_table.organization_id). Team-level
authorization already gates this path, so team membership is sufficient.
The membership check still runs when a caller assigns an organization_id
that did not come from the key's team, preserving the IDOR protection.
Adds regression tests covering both the team-inherited (allowed) and
foreign-org (still blocked) cases.
Co-authored-by: Cursor <cursoragent@cursor.com>
* test(key_generate): cover mismatched team org IDOR path on generate
Add test_generate_key_foreign_org_with_mismatched_team_still_enforces_membership
for the case where a team is present but request organization_id differs from
team_table.organization_id. Enterprise inheritance is no-op'd in the test so
the guard is exercised directly; membership validation must still run.
Addresses Greptile review on #29310.
Co-authored-by: Cursor <cursoragent@cursor.com>
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
(cherry picked from commit b11833c737)
