When a proxy fronts Claude Code (which always sends `output_config.effort`)
at a pre-4.5 Anthropic model — haiku-3, sonnet-3.5, opus-3, sonnet-4 — the
forwarded knob causes a forced 400 the client can't fix. Gating a strip
behind the existing `drop_params` flag lets operators opt into silent
fixup once and stop worrying about per-model param hygiene.
Default (`drop_params=False`) still forwards and surfaces the provider's
error, preserving the strict, debuggable contract from #27074.
Per https://platform.claude.com/docs/en/build-with-claude/effort the
supporting set is Opus 4.5+, Sonnet 4.6+, and Mythos Preview; everything
else is dropped (with a verbose_logger warning so the strip is visible).
Recognition uses model-name patterns plus a fallback to any
`supports_*_reasoning_effort` flag in the model map for forward
compatibility with new entries.
https://claude.ai/code/session_01WjHq31rvXT6xYNdVmSJvRp
Follow-up bugs surfaced by the QA sweep on PR #27039
(https://github.com/BerriAI/litellm/pull/27039#issuecomment-4363363610).
1. Stop stripping output_config.effort on Bedrock + Vertex adaptive routes.
- Vertex AI Claude 4.6/4.7 accepts output_config.effort on rawPredict
(verified end-to-end against us-east5 / global). The strip helper now
no-ops for effort.
- Bedrock Converse routes output_config into additionalModelRequestFields
for anthropic base models so the requested adaptive tier (low/medium/
high/xhigh/max) actually reaches the wire instead of all collapsing to
identical thinking.
- Bedrock Invoke chat transformation (AmazonAnthropicClaudeConfig) stops
popping output_config from the post-AnthropicConfig request body.
- Bedrock Invoke /v1/messages allowlist (BedrockInvokeAnthropicMessagesRequest)
now lists output_config so the runtime allowlist filter forwards it.
2. Validate effort across Bedrock Converse so 'disabled' / 'invalid' / '' /
unsupported tiers (xhigh/max on Sonnet 4.6 or budget-mode 4.5 models)
surface as a clean 400 BadRequestError instead of 500.
3. ValueError -> BadRequestError throughout (AnthropicConfig.map_openai_params,
_apply_output_config, AmazonConverseConfig._handle_reasoning_effort_parameter).
Empty-string effort is now rejected (was silently passing the
'if effort and ...' short-circuit).
4. Floor reasoning_effort='minimal' at the Anthropic provider minimum
(1024 budget_tokens) via new ANTHROPIC_MIN_THINKING_BUDGET_TOKENS so it's
a usable tier on direct Anthropic / Azure AI Anthropic / Vertex AI Anthropic /
Bedrock Invoke (all of which 400 below 1024).
5. model_prices: dedupe duplicate supports_max_reasoning_effort key on
claude-opus-4-7 / claude-opus-4-7-20260416.
Adds regression tests across all five affected paths; existing tests asserting
the silent-strip behavior were updated to reflect the new pass-through and
clean 400 surfaces.
Co-authored-by: Mateo Wang <mateo-berri@users.noreply.github.com>
Setting reasoning_effort="none" on Anthropic chat models (direct, Bedrock
Invoke, Bedrock Converse, Vertex AI Anthropic, Azure AI Anthropic) crashed
LiteLLM with:
litellm.APIConnectionError: 'NoneType' object has no attribute 'get'
Both the Anthropic chat transformation and Bedrock Converse called
``AnthropicConfig._map_reasoning_effort`` and assigned the ``None`` it returns
for ``"none"`` directly to ``optional_params["thinking"]``. Downstream
``is_thinking_enabled`` then did ``optional_params["thinking"].get("type")``
and crashed.
Pop ``thinking`` (and on Claude 4.6/4.7, ``output_config``) instead of
assigning ``None``, restoring the documented contract that
``reasoning_effort="none"`` means "do not enable thinking". This also
prevents downstream Anthropic 400s ("thinking: Input should be an object",
"output_config.effort: Input should be ...") if the bug were ever masked.
Verified end-to-end against the live Anthropic API and Bedrock Converse
on claude-opus-4-{5,6,7} and claude-sonnet-4-6, plus Bedrock Invoke for
Claude 4.5/4.6. Vertex AI Anthropic and Azure AI Anthropic inherit the
fixed ``map_openai_params`` from ``AnthropicConfig`` and need no further
changes.
Adds litellm.disable_vertex_batch_output_transformation (default False).
When True, afile_content returns raw Vertex predictions.jsonl untouched
so users that parse candidates/modelVersion directly are not broken.
The method was overwriting logging_obj.optional_params, logging_obj.model,
and logging_obj.start_time on the caller's Logging instance. When invoked
from llm_http_handler.py's generic framework path, the framework's own
logging_obj (which already went through pre_call) had its properties
clobbered, causing model and start_time to reflect the last batch line's
values rather than the original call context.
Fix: create a fresh local Logging instance for the per-line transformation
instead of mutating the incoming logging_obj. The caller's object is now
left entirely untouched regardless of whether a logging_obj was passed in
or not.
Regression tests added to verify model, start_time, and optional_params
are not mutated on the caller's logging_obj.
Co-authored-by: Sameer Kankute <Sameerlite@users.noreply.github.com>
Conflict resolution for #26968 dropped the `Iterator` typing import
(NameError at module load), left a dead `fallback_models = cast(...)`
block, and the new tests called `_enforce_key_and_fallback_model_access`
without the now-required `request` kwarg.
* fix(proxy): re-validate user_id ownership after /user/info re-parses query
The route-level access check in `RouteChecks.non_proxy_admin_allowed_routes_check`
reads `request.query_params.get("user_id")`, which decodes literal `+` to
spaces. The endpoint then re-parses the raw query string with `urllib.unquote`
in `get_user_id_from_request` to preserve `+` characters (so plus-addressed
emails work as user_ids). Those two paths produce different ids: a caller
who registered a user_id containing a literal space could pass the route
check and then read another user's row by sending the encoded `+` form.
Add `_enforce_user_info_access` and call it after `_normalize_user_info_user_id`
returns the final id. Proxy admin / view-only admin still bypass; everyone
else must match the resolved user_id (or have no user_id, which falls back
to the caller's own id later in the handler).
Tests cover the admin bypass, owner-match path, and the cross-user lookup
that this change blocks.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(proxy): apply user_info ownership check to PROXY_ADMIN_VIEW_ONLY
`_enforce_user_info_access` was bypassing both PROXY_ADMIN and
PROXY_ADMIN_VIEW_ONLY, but the upstream route check in
`RouteChecks.non_proxy_admin_allowed_routes_check` only treats
PROXY_ADMIN as a true admin for the `/user/info` route — view-only
admins go through the `user_id == valid_token.user_id` enforcement
along with regular users. Mirroring that asymmetry left the same
encoded-`+` bypass open for view-only admins whose user_id contains a
literal space.
Drop the PROXY_ADMIN_VIEW_ONLY exemption so the post-decode re-check
matches the upstream rule. Update tests: a view-only admin must now
be blocked from cross-user lookups but still allowed to read their
own row.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: yuneng-jiang <yuneng@berri.ai>
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(batches): count non-chat tokens and validate every model in batch file
Two security control bypasses on POST /v1/batches:
1. `_get_batch_job_input_file_usage` only summed tokens for
`body.messages` (chat completions). Embedding (`input`) and text
completion (`prompt`) batches reported zero, letting massive
non-chat workloads slip past TPM rate limits. Extend the counter
to handle string and list shapes for both fields.
2. The batch input file was forwarded to the upstream provider
without inspecting the models named inside the JSONL — only the
outer `model` query parameter was checked against the caller's
allowlist. A caller restricted to gpt-3.5 could submit a batch
targeting gpt-4o and the upstream would execute it under the
proxy's shared API key.
Add `_get_models_from_batch_input_file_content` (returns the
distinct `body.model` values) and call it from
`_enforce_batch_file_model_access` in the pre-call hook, which runs
each model through `can_key_call_model` so the same allowlist
semantics (wildcards, access groups, all-proxy-models, team aliases)
the proxy enforces on `/chat/completions` apply here too. Any
unauthorized model raises a 403 before the file is forwarded.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
* fix(batches): count pre-tokenized prompt/input shapes, classify 403 logs
Two follow-ups from the Greptile review on the batch validation PR:
1. P1 TPM bypass via integer token arrays. The OpenAI batch schema
accepts ``prompt`` and ``input`` as ``list[int]`` (a single
pre-tokenized prompt) or ``list[list[int]]`` (multiple) in addition
to the string and ``list[str]`` shapes. Pre-fix only the string
shapes were counted, so a caller could submit a batch with hundreds
of millions of pre-tokenized tokens and the rate limiter would
record zero. Extract the per-field logic into
``_count_prompt_or_input_tokens`` and count each int as one token.
2. P2 access-denial logs were indistinguishable from I/O failures.
``count_input_file_usage`` caught every exception under a generic
"Error counting input file usage" message, so an intentional 403
from ``_enforce_batch_file_model_access`` looked the same in the
logs as a missing file or a Prisma timeout. Catch ``HTTPException``
separately and log 403s at WARNING level with a security-relevant
message before re-raising.
Tests cover the new shapes: single ``list[int]``, ``list[list[int]]``
(the worst-case bypass vector), and embeddings ``input`` with
pre-tokenized arrays.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
---------
Co-authored-by: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Reset the policy ID index during policy engine test cleanup so stale policy versions cannot leak between tests.
Co-authored-by: Cursor <cursoragent@cursor.com>
The router_code_coverage CI check requires every function in router.py to
be referenced by at least one test under tests/{local_testing,
router_unit_tests,test_litellm} in a file with "router" in its name.
The recently-extracted helper had no direct test, so the check failed
with "0.45% of functions in router.py are not tested".
Add a focused test that exercises the four return paths: model already
in self.model_names, no fallback applies, pattern-router match, and
default_deployment substitution (also asserting the stored default
isn't mutated).
https://claude.ai/code/session_019AVp1XL7RT9RxRe4qRLkay
Mark runtime-created policies and attachments initialized so global policy attachments created from the policy builder apply immediately without requiring a restart.
Co-authored-by: Cursor <cursoragent@cursor.com>
When _get_all_deployments returns 0 candidates and the litellm-model
fallback branch (_get_deployment_by_litellm_model) finds deployments that
the access-group filter then empties, _access_group_filter_emptied_candidates
remained False (it was captured before that branch ran). The router would
then proceed to default fallbacks; the fallback model could have no
access_groups and short-circuit the filter, silently serving a caller
blocked by access-group restrictions.
Update the flag inside the litellm-model branch when filtering empties a
non-empty candidate set so the default-fallback guard still triggers.
When a team grants /key/list via team_member_permissions, non-admin members
should see all keys for that team — same as a team admin. Previously the
classification in list_keys() only checked admin status, so permitted
members fell into the service-account-only path and could not see other
members' personal keys. Routes those members into the full-visibility set.
Greptile P1: the unsafe-method branch of `_check_proxy_admin_viewer_access`
ended with a blanket `if route in management_routes: return`. That set is a
mix of reads (info/list — handled via the safe-method GET branch above) and
writes. The fallback let Admin Viewer POST to write endpoints not enumerated
in `_ADMIN_VIEWER_BLOCKED_WRITE_ROUTES`, including:
- /team/block, /team/unblock, /team/permissions_update
- /jwt/key/mapping/{new,update,delete}
- /key/bulk_update
- /key/{key_id}/reset_spend
Remove the fallback. The two remaining allow sets (admin_viewer_routes and
global_spend_tracking_routes) are both read-only, so removal does not affect
the legitimate POST-as-read cases (e.g. /spend/calculate, which is in
spend_tracking_routes ⊂ admin_viewer_routes).
Tests:
- 8 new parametrized cases pinning each previously-leaking management write
endpoint to 403 on POST for PROXY_ADMIN_VIEW_ONLY.
The reservation path (PR #26845) atomically pre-fills `spend:user:{user_id}`
and admits at the strict-`<` boundary. The legacy `_PROXY_MaxBudgetLimiter`
pre-call hook re-reads the same counter with `>=`, so a reservation that
fills the counter to exactly `max_budget` (e.g. a request without a
`max_tokens` cap that falls back to reserving the smallest remaining
headroom) is rejected by the hook even though the reservation already
admitted it.
Skip the hook when the request's active `budget_reservation` covers
`spend:user:{user_id}`. The reservation is the source of truth for that
counter cross-pod; the legacy `>=` path remains in place for requests
without a reservation (e.g. paths that bypass the reservation entirely).
Reproduces as `tests/otel_tests/test_prometheus.py::test_user_budget_metrics`
on a fresh user with `max_budget=10` calling `fake-openai-endpoint` without
`max_tokens`. Adds focused unit coverage in
`tests/test_litellm/proxy/hooks/test_max_budget_limiter.py`.
Greptile flagged two follow-ups on the OpenAPI/local-registry pre-call
check:
1. **P1 runtime crash via None proxy_logging_obj.**
`kwargs.get("proxy_logging_obj")` is `None` on the MCP entry path,
and `pre_call_tool_check` calls `proxy_logging_obj._create_mcp_request_object_from_kwargs`
unconditionally after the security checks, which would have crashed
every legitimate call with `AttributeError`. Source the logging
object from `litellm.proxy.proxy_server` the same way
`_handle_managed_mcp_tool` already does.
2. **P2 authorization-bypass window when mcp_server is None.**
Previously the new check was guarded by `if mcp_server is not None`,
so any local tool whose registry entry had no resolvable server (a
startup-race window before `_initialize_tool_name_to_mcp_server_name_mapping`
completes, or an orphaned registry entry) ran without the security
check. Tools registered via openapi_to_mcp_generator are always tied
to a server, so a missing one is a configuration/timing fault — fail
the call with 503 instead of dispatching unguarded.
Tests: existing two pass with an added assertion that
`proxy_logging_obj` is non-None at the call site, plus a new test that
covers the 503 deny branch when the tool→server mapping is missing.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
