_independent_snapshot did one whole-dict copy.deepcopy and returned None on
any failure. Every real proxy request carries data["litellm_logging_obj"]
(a Logging instance nesting a live OTel span with a real lock) by the time
pre_call_hook runs, which can never be deep-copied, so the snapshot failed
on every real request and silently fell back to the live, unisolated data
with no warning -- defeating the entire feature in production while every
existing test (none of which set litellm_logging_obj) kept passing.
Rework the helper to deep-copy each top-level key independently, falling
back to the original reference only for the specific key that fails, same
crash tolerance as safe_deep_copy's own per-key fallback. It never returns
None now; only the keys scan_raw_request actually depends on (messages/
input, metadata/litellm_metadata) need to be genuinely independent.
The during_call branch for a mixed violation under on_flagged=inject_system_message
unconditionally masked and reassigned data["messages"], even for a pure
prompt-injection violation with zero PII, unlike async_pre_call_hook which
already gates the same call behind _breakdown_has_pii_violation. The
unconditional reassignment touched shared request state during a hook
documented as racing with the concurrent LLM dispatch, for no reason when
there was nothing to mask.
safe_deep_copy can return the original object under litellm.safe_memory_mode,
or alias a per-key reference on copy failure. Under that mode, the
scan_raw_request comparison baseline aliased raw_request_snapshot (and
therefore the live request), letting mark_pre_call_hook_ran write a
premature execution marker that a deployment-level guardrail sharing the
same name would read as "already ran" and skip. Also affected the feature's
core isolation guarantee: input_data itself could alias the live request
under the same mode. Replace every scan_raw_request snapshot with
_independent_snapshot, which never returns an alias, only a genuine copy
or None.
_process_guardrail_callback always returns a dict once a guardrail runs
(mark_pre_call_hook_ran unconditionally stamps bookkeeping metadata), so
comparing the result to non-None warned on every request even when the
guardrail never touched the payload. Compare against a bookkeeping-only
baseline instead, so only an actual content mutation triggers the warning.
The lazy OpenAPI snapshot (litellm/proxy/_lazy_openapi_snapshot.json) and
the derived dashboard schema.d.ts had drifted stale relative to the
guardrail config model changes across this PR's rounds (advisory mode,
scan_raw_request, and upstream additions picked up by rebasing).
Regenerated via the CI's own documented fix:
uv run python -m litellm.proxy._lazy_openapi_snapshot
npm run gen:api (via make check)
- Use safe_deep_copy instead of a bare deepcopy for the raw-request
snapshot; request payloads commonly carry unpicklable objects (e.g. an
otel span in metadata), which previously raised on every guarded
request when tracing was enabled (Bugbot, High).
- Only compute the snapshot when a guardrail actually opted in, and take
it before _maybe_execute_pipelines runs, so a pipeline-mutated payload
can't hide a violation from a scan_raw_request guardrail outside the
pipeline (veria-ai).
- Log a warning when a scan_raw_request guardrail returns a modified
payload, since that mutation is discarded and the combination is
otherwise silently exploitable for a masking-capable integration
misconfigured this way (veria-ai).
Maintainer finding on BerriAI/litellm#34940: guardrails for the same hook
run sequentially over one shared, progressively-mutated request dict, so
declaring a masking guardrail before a blocking one hides the violation
from it (200 vs 400 depending purely on YAML order).
scan_raw_request opts a guardrail into always evaluating a snapshot taken
before any guardrail in the hook ran, regardless of its declared position.
Same contract as run_in_parallel: block-only, its own mutations discarded.
Verified live: real proxy, real Gemini call, two custom guardrails (a
redactor then a blocker). Same request, same declared order -- without the
flag the blocker never sees the raw secret (200); with it, the blocker
correctly rejects before any provider call (400).
This session found three real bugs a human maintainer caught after eight
rounds of bot review and live-proxy verification all missed them. Add a
standing instruction to write learnings.md entries the moment a root
cause is understood, in both the repo-wide file and any relevant skill's
own file, instead of relying on being asked.
- Add match= to a too-broad pytest.raises(ValueError), and suppress the
new TQ008 mocker.patch findings (same pattern already used by sibling
scenarios in this test).
- test_init_guardrails_v2_skips_invalid_guardrail_instead_of_crashing_boot
used mode='during_call' + on_flagged='inject_system_message' as its
invalid-config example; that combination is now accepted, so swap in
the payload/breakdown-missing case and add a test confirming during_call
advisory mode constructs successfully.
- Gate advisory_system_message template validation on on_flagged=
'inject_system_message', since block/monitor mode never reads it.
- Allow on_flagged='inject_system_message' with mode='during_call' at
construction/hot-reload instead of rejecting it; async_moderation_hook
already degrades gracefully (masks if possible, else logs a warning).
- reinitialize_guardrail now restores the previous live instance when the
new config fails to initialize, instead of leaving the guardrail deleted
entirely with nothing enforcing it.
- PATCH /guardrails/{id} rolls back the DB write and returns 422 when the
in-memory sync rejects the new config, instead of persisting a config
that never actually took effect and returning 200.
- Qualifire now rejects on_flagged values it doesn't implement (only
Lakera should accept 'inject_system_message'; LitellmParams flattens
the field across every guardrail config mixin).
_has_responses_instructions treated any non-empty data["instructions"]
as unsafe to mask regardless of skip_system_message_in_guardrail, even
though that flag excludes the instructions-derived synthetic system
message from what Lakera ever inspects. PII detected purely in the
maskable non-system content was force-blocked instead of masked.
Also fixes pre-existing LIT010 (missing Final) violations in
_has_responses_instructions, _breakdown_has_pii_violation, and
async_post_call_success_hook that the rebase's lowered budget ceiling
now flags.
Advisory mode's mixed-violation masking safety net can only redact
detected PII when Lakera's response carries both the breakdown (to
detect a PII hit at all) and payload (the location data to mask by).
payload=False or breakdown=False alongside on_flagged='inject_system_message'
silently forwarded raw PII next to the advisory note. Reject that
combination at construction and hot-reload time instead.
Bugbot finding: gating the entire inject_system_message branch on
is_multimodal_input hard-blocked every flagged request on Responses
instructions, combined messages+input, or multimodal content, including
a prompt-injection-only violation with no PII at all. Masking safety only
matters when there's actual PII to mask; a violation with no PII needs no
masking, so the advisory should still be delivered normally.
Only degrade to blocking when the breakdown actually contains a PII
detection and masking isn't safely possible. Otherwise, mask whatever's
maskable (if any) and deliver the advisory as before.
on_flagged="inject_system_message" only masked when a violation was
PII-only; a mixed violation (PII plus a non-PII flag like prompt injection)
fell straight through to the advisory branch with the raw PII still in
place, in both async_pre_call_hook and async_moderation_hook. Mask whatever
Lakera returned location data for before appending or logging the advisory,
so a mixed violation never ships raw PII just because something else was
also flagged.
Also degrade to blocking, same as block mode already does, when nothing
can be safely masked at all (multimodal content, or messages combined with
a Responses API input field) instead of showing an advisory note next to
raw, unredacted content.
Widened call_v2_guard/_mask_pii_in_messages/the write-back helper's message
parameters from list to Sequence to match what's actually passed through
from _filter_skipped_messages, instead of duplicating list(...) casts at
every call site.
Greptile finding: the masking scope helper compared roles case-sensitively
while filter_messages_by_skip_flags (used to build what's actually sent to
Lakera) normalizes casing, so an uppercase-cased "System"/"TOOL" role
survived the scope filter but was excluded from the inspected list. The
resulting length mismatch raised inside the strict positional zip, turning
a maskable PII-only violation into an unhandled request failure. Lowercase
the role comparison to match.
Also, per veria-ai's finding that a skipped invalid guardrail now fails
open: log it at error level with an explicit note that the proxy is
starting without that guardrail, so it's not mistaken for routine info.
Parameterize the write-back helper's dict param and suppress the two new
lint rules that landed on the base while this branch was in flight: TQ008
(patching an internal collaborator) for two pre-existing tests unrelated to
this change, and LIT001 for a param that genuinely needs to mutate the
caller's request dict in place.
Mask-in-place degraded to a hard block for any message carrying a field
beyond role/content (tool_call_id, tool_calls, name, cache_control), for a
message excluded by skip_system_message_in_guardrail/skip_tool_message_in_guardrail,
or for a message with no inspectable text, since it rewrote data["messages"]
wholesale from a synthetic role/content-only list built for the Lakera API
call. That made masking effectively unusable for any real tool-calling
conversation and made the skip flags flip every PII-only violation to a hard
block instead of masking just the in-scope text.
Replace the wholesale rewrite with a scope-index merge, reusing the same
merge_guardrailed_scoped_messages helper the OpenAI/Anthropic guardrail
translation handlers already use for this: patch content in place on a copy
of each original message actually sent to Lakera, and leave every
skipped/no-text/out-of-scope message untouched at its original position.
This also fixes on_flagged="inject_system_message" (advisory mode) shipping
raw unmasked PII to the model: a PII-only violation is now masked the same
way regardless of on_flagged, and the advisory note is reserved for flags
masking can't resolve on its own.
Addresses maintainer-reported regressions on BerriAI/litellm#34940.
init_guardrails_v2 had no try/except around initialize_guardrail, so a
guardrail whose litellm_params fail validation at construction time (for
example Lakera's on_flagged=inject_system_message combined with
mode=during_call, or a malformed advisory_system_message template) raised
uncaught and crashed the entire proxy at startup, taking down every other,
correctly-configured guardrail in the list. Catch ValueError/TypeError per
guardrail, log a warning, and skip it, matching the same pattern already
used for the DB-driven guardrail-creation path in guardrail_endpoints.py.
Squashed rebase of bugfix/lakera-v2-skip-system-tool-messages onto latest
litellm_internal_staging (900+ commits ahead; a commit-by-commit rebase hit
repeated conflicts against the same files across earlier review-round
commits, so the branch's cumulative diff was reapplied in one pass instead).
Adds skip_system_message_in_guardrail/skip_tool_message_in_guardrail support
to Lakera v2, a third on_flagged: "inject_system_message" advisory mode, and
the associated masking-safety-guard hardening (multimodal content, non-
maskable message fields, combined messages+input, and structured Responses-
API input in advisory delivery) found across this PR's review rounds.
* fix(langfuse): warn and drop invalid LANGFUSE_TRACING_ENVIRONMENT instead of failing requests
* fix(langfuse): treat a dynamic environment equal to the raw deployment value as redundant
* fix(guardrails): add fail-open mode to CrowdStrike AIDR guardrail
Add a fail_on_error param (default True, preserving existing behaviour) to
the CrowdStrike AIDR guardrail, mirroring model_armor and generic_guardrail_api.
When fail_on_error=False the guard fails open only on server errors (5xx) and
connectivity failures, so the request proceeds unmodified. Caller-controlled
4xx responses and result.blocked policy blocks always fail closed. The
applied-guardrails header is recorded even on the fail-open path.
* fix(guardrails): fail open AIDR 4xx
* refactor(guardrails): isolate AIDR fail-open
* style(guardrails): format AIDR fail-open
* ci: satisfy unit workflow timeout invariant
* refactor(guardrails): accept AIDR mappings
* test(guardrails): inject AIDR HTTP client
* fix(guardrails): harden AIDR fail-open against delivered verdicts and record fail-open status
Reads the blocked verdict from the raw body before guard_output validation so schema drift or a changed verdict type cannot fail open past a delivered block. A transformed response that cannot be parsed fails closed so delivered redactions are never dropped. Fail-open runs record guardrail_status guardrail_failed_to_respond with timings instead of success. Restores the fail-open behavior tests dropped mid-PR and reverts the payload Mapping widening
* test(guardrails): cover fail_on_error wiring and fail-closed default for CrowdStrike AIDR
* chore(guardrails): annotate the transformed-drift detail payload for the LIT002 budget
---------
Co-authored-by: abrekhov <abrekhov@users.noreply.github.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(ui): dry-run an auto-router config against the backend before saving it
Both auto-router forms built a payload and posted it, so anything the write gate
refused came back as a raw 400 with the backend's message buried in it. They now
POST the exact payload to /auto_router/validate_complexity_router_config first
and surface its verdict inline.
One dryRunRejection owns the gate, and it reads valid alone. The verdict's two
fields arrive independently, so gating on the error message would let a rejection
that carried none through to the write. A transport failure fails open as valid,
leaving the write gate authoritative rather than blocking a save on a flaky
network.
Applies to every auto-router, built-in tiers included.
* fix(ui): hold the auto-router create closed for the full dry-run and create sequence
A second submit while the dry-run round-trip was pending started another
create against the non-idempotent /model/new. The submit handler now
refuses re-entry and the button disables for the whole sequence, matching
the edit modal's loading guard. Also drops the explanatory comments this
PR had added.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
Route records below WARNING to stdout (WARNING and above stay on stderr),
emit ANSI color codes only when both streams are a TTY (honoring NO_COLOR),
and parse JSON_LOGS strictly so JSON_LOGS=false no longer enables JSON logs.
* fix(presidio): chunk oversized text before /analyze so large content blocks do not fail
The Presidio PII guardrail sent each content block to the analyzer as a
single /analyze call with no size check. Analyzer deployments commonly cap
the request body (the reporting deployment rejects bodies over 1,000,000
bytes with HTTP 413), so large blocks failed closed, and analyzer latency
grew linearly with payload size.
analyze_text now splits texts larger than presidio_analyze_chunk_size_bytes
(default 500,000 UTF-8 bytes, configurable per guardrail) into overlapping
chunks, analyzes them concurrently, remaps each detection's start/end onto
the original text, and deduplicates detections from the overlap regions.
Anonymization, blocked-entity checks, score filtering, numbered-token
unmasking, telemetry, and the dashboard entity positions all consume the
remapped global offsets unchanged.
Resolves LIT-4785
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(presidio): review-round hardening for chunked analyze
- measure the chunk budget on the JSON-serialized text (non-ASCII escapes
expand beyond raw UTF-8, so a raw-byte budget could still exceed the
analyzer body limit)
- share the chunk fan-out semaphore per event loop and instance instead of
per call, so many oversized blocks cannot multiply concurrent analyzer
calls
- apply configured score thresholds and deny list per chunk BEFORE overlap
resolution, so a below-threshold span cannot displace a detection the
thresholds keep
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
---------
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
The /v1/messages bridge decided a Claude target could take `reasoning_effort` from
the model name, which says nothing about the params the provider in front of it
accepts. Snowflake serves Claude over the Anthropic dialect and declares `thinking`
alone, so `get_optional_params` raised `UnsupportedParamsError` before the request
reached the wire: every adaptive request carrying an effort tier turned a 200 into
a 400 for all seven of its Claude entries.
The tier is now offered only where the target declares the param, reading the same
`get_supported_openai_params` the sibling `_supports_prompt_cache_key` reads twelve
lines up. A target declaring neither carrier keeps its bare `thinking` block, which
is what this bridge sent before it carried a tier at all.
Without a resolved provider the tier stays behind rather than being offered blind.
Resolving one from the model's prefix instead would run an OAuth device flow for
github_copilot and chatgpt, blocking for minutes, and one of the two callers in that
position is a logging callback. The copilot case is pinned by a test.
* feat(ui): session-level cache observability in request logs
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix: guard cache_hit filter against non-string defaults in direct calls
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* refactor(ui): drop redundant cache_hit field comment
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
A complexity-router setting placed beside complexity_router_config, or inside a
tier entry's litellm_params, is read by nobody: the router loads its settings only
from litellm_params.complexity_router_config. It does not stay inert. The
alias-marker forwarding and the per-tier param spread carry every unrecognized key
onto the outbound request, and all_litellm_params only knows the outer names, so
the key reaches the provider as an unknown body field and every call through that
model group fails with an error naming an internal config key.
Guard the whole set, derived from ComplexityRouterConfig.model_fields so a field
added later is covered, and scoped to complexity-router deployments because the
names only mean this there (embedding_model is a legitimate flat param on an
s3_vectors vector store). Scope is read from the same merged field view the naming
check is judged on, so a router named only by its default model is in scope and a
field added to the required-field table is covered without another edit. The write
endpoints reject with a 400 naming the keys and where they belong, config.yaml
refuses to start for the same reason max_agentic_loops does, and a tier entry is
judged by the config model itself.
An already-stored deployment keeps loading, so an upgrade cannot take a running
gateway down over a row that was written before the gate existed.
Two lazily loaded models changed without their generated artifacts being
regenerated, so check-ui-api-types has been red on every branch off staging.
The snapshot that /openapi.json serves for unloaded features was missing
ChatCompletionToolReferenceObject, and the dashboard types were missing
aws_external_id. The snapshot step runs first and short-circuits, so only the
first one was visible until it was fixed.
Both files are regenerated with `python -m litellm.proxy._lazy_openapi_snapshot`
and `npm run gen:api`, no hand edits.
* feat(alerting): add native Microsoft Teams alerting destination
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(alerting): preserve active destinations on MS Teams save and confirm health test delivery
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(ui): read persisted alerting destinations at MS Teams save time
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
---------
Co-authored-by: yassin <yassin@berri.ai>
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
/v1/messages forwarded `thinking` verbatim for a Claude-family model and then returned,
carrying `output_config.effort` only when the model string started with a Bedrock prefix.
Every other bridged provider got a bare adaptive thinking block, so the caller's effort did
nothing: max and minimal produced byte-identical upstream bodies.
Send those targets the tier as `reasoning_effort`, which is the param they take. Bedrock keeps
taking `output_config`, since the two are not interchangeable there: an application inference
profile ARN resolves to no chat config, so `reasoning_effort` is dropped and the tier vanishes,
and a provider that rebuilds `output_config` from it overwrites a caller-set `thinking.display`
on the way. The tier stays a plain string, the summary already travelling inside the forwarded
`thinking` block. Adaptive with no tier, and budgeted thinking, both stay exactly as they were.
Kimi K3 accepts exactly low, high and max, defaults to max, and always thinks.
The map could not say that: medium and high have no supports_*_reasoning_effort
flag because every other reasoning model takes them, so the ten kimi-k3 entries
carried supports_reasoning alone and resolved to unknown. The dashboard then fell
back to a capability-blind level list that deliberately omits max, which is why a
kimi-k3 tier cannot be set to max thinking today.
Add reasoning_effort_levels, an array key in the shape the map already uses for
supported_endpoints and supported_modalities. Where present it is read first and
wins whole; every other entry keeps answering through the per-level flags,
unchanged. It is deliberately a different name from the computed
ModelGroupInfo.supported_reasoning_efforts, which stays derived from a group's
deployments and is never seeded from one deployment's model_info.
The levels are per entry rather than per model, because the deployments differ:
Moonshot, Together, Fireworks and Azure Foundry all forward the level unchanged
and get the model's own low/high/max, while Perplexity documents a six-value
enum it maps down internally and gets that. The /v1/messages degradation chain
consults the same declaration, so the level the map advertises is the level that
path forwards.
