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* test(e2e): add other suite covering master-key auth and health lifecycle Covers the other.* holding-pen cells that were uncovered: master-key valid_allows/invalid_denied on the admin /user/list gate, and the lifecycle probes liveness.ping, readiness.public_probe, readiness.reports_db_status, and readiness_details.authenticated_diagnostics. New tests/e2e/other/ suite on the shared ProxyClient; the health probes send no auth header to prove the public routes need no credential, and the details route is asserted to reject an anonymous caller while exposing version/db diagnostics to the master key. * test(e2e): cover block_code_execution and openai_moderation guardrails Extends the guardrails suite with two built-in guardrails registered per request (default_on=False, opted in via the chat body's guardrails selector) so neither intercepts unrelated traffic on the shared proxy. block_code_execution.pre_call.blocks: a python code block plus a run-this request is intercepted with the canned content-blocked message and the model never runs, while the same code block asked about with don't-run-it reaches the model. Verified live. openai_moderations.pre_call.blocks: a flagged prompt is rejected 400 naming the moderation policy while a benign prompt passes. The guardrail calls OpenAI's moderation API; verifying it needs an OpenAI key with moderation quota (this account currently 429s the moderation endpoint). Adds a shared create_backend_model helper and a generic register() plus per-request guardrails/max_tokens on the client so more built-ins can reuse the same path. * test(e2e): cover presidio PII masking (pre_call + post_call) Registers a presidio guardrail per request (default_on=False) with the analyzer/anonymizer bases supplied in the registration params, so the test controls its own dependency and needs no proxy restart. presidio.pre_call.masks: a repeat-verbatim request comes back with the <EMAIL_ADDRESS> placeholder and never the raw email, proving the prompt was anonymized before the model saw it. presidio.post_call.masks: with apply_to_output the model's own emitted email is masked on the way out, so the caller never receives the raw value. Both verified live against real presidio analyzer + anonymizer containers. logging_only is intentionally not covered: /spend/logs exposes no prompt messages to read back the masked log, and a logging_only run also masked the response, contradicting its contract; noted in the module docstring for a follow-up. * test(e2e): cover presidio logging_only masking via OTEL read-back Adds the third presidio cell, guardrail.presidio.logging_only.masks. The logging_only contract (mask what is logged, do not block) is verified by reading the request's gen-AI span back from the real OTEL destination: the span's gen_ai.input.messages attribute carries the <EMAIL_ADDRESS> placeholder, never the raw email, and the call itself is not blocked. Reads the trace via the shared OtelReader, promoted from logging/ to the suite root so both suites use it. The masked prompt is polled to a deadline because logging_only masks the payload asynchronously and the span can briefly export before the mask lands. Drops the throwaway chat_send in favor of the existing transport.send for the call-id capture. * fix(e2e): tolerate cross-pod guardrail sync delay in team-opt-out test Stage runs multiple gateway pods behind the shared key. POST /guardrails registers a new default-on guardrail in-process immediately only on the pod that served the create call; every other pod picks it up on its next periodic DB sync (proxy_server.py, every 30s), so the very next chat call can race a pod that has not synced yet. Poll to a 40s deadline instead of asserting on the first response, matching the existing pattern in test_budget_reset_advances_e2e.py. * test(e2e): cover a guardrail on the MCP tool-call path (content_filter pre_mcp_call) Adds guardrail.litellm_content_filter.pre_mcp_call.blocks: against the real Datadog MCP server, a content_filter guardrail configured mode=pre_mcp_call blocks a banned keyword in an MCP tool call's arguments with HTTP 400 attributed to the pre_mcp_call hook, and lets a clean argument reach the upstream server. The guardrail attaches with default_on because per-key/request guardrail selection is dropped from the synthetic MCP request the hook sees; the banned keyword is unique per run so default_on only intercepts this test's own call. mode must be pre_mcp_call - a pre_call config silently no-ops on tools/call because the event type is rewritten for call_mcp_tool. Drives the tool directly via /mcp-rest/tools/call for a deterministic check of the same pre_mcp_call enforcement the OpenAI-SDK chat path hits when a model invokes an MCP tool. * fix(e2e): mid-conversation messages test uses client.proxy not client.gateway EndpointsClient exposes .proxy after the Gateway->ProxyClient rename; the mid-conversation system test still referenced .gateway, which fails the e2e basedpyright gate. Aligns it with the rest of the harness. * test(e2e): address review on the guardrail coverage MCP tool-call guardrail: poll the banned call until the guardrail is enforced instead of asserting on the first call, so the control-plane -> data-plane guardrail sync cannot race the check into a false pass-through; add a repeat banned call after enforcement to guard against a partial-propagation state. OpenAI moderation: distinguish a moderation-endpoint 429 (rate limit / no moderation quota) from a guardrail failure, so an account-capability gap reads as such rather than as "did not block". Runs green with a moderation-capable key. * test(e2e): close partial-propagation false-pass in MCP guardrail block test The single post-block repeat call could be load-balanced back to the same already-synced data-plane pod, so the test could pass while another pod still lacked the guardrail and let the banned MCP call reach Datadog. Anchor a wait to the guardrail create time (every pod is guaranteed to have DB-synced only after a full ~30s sync interval), then require the banned call to stay blocked across several attempts; a pass-through after that window is a real leak, not a race. * test(e2e): drop xfail-style rate-limit branch from openai_moderation test OpenAI's /v1/moderations is free and returns 200 with the env key (verified directly), so the RateLimitedError branch mislabeled the failure: a 429 there is insufficient_quota (no account billing), not throttling. The branch also only printed a softer message before failing anyway, an xfail-in-disguise the e2e rules forbid. A 429 now falls through and fails loudly with the full result.
34 lines
8.4 KiB
YAML
34 lines
8.4 KiB
YAML
# Guardrail enforcement (behavior features). Grounded in litellm/proxy/guardrails/guardrail_hooks/.
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# Rolls up into the "Logging & Guardrails" dashboard module together with logging.*
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- {id: guardrail.presidio.pre_call.masks, module: guardrail, tier: P0, hook_point: pre_call, assertions: [masks], exercised_on: [chat_completions, messages], source: "guardrail_hooks/presidio.py", rationale: "PII masking pre-call; data-leak blast radius"}
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- {id: guardrail.presidio.post_call.masks, module: guardrail, tier: P0, hook_point: post_call, assertions: [masks], exercised_on: [chat_completions, messages], source: "guardrail_hooks/presidio.py", rationale: "Mask PII in model output"}
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- {id: guardrail.presidio.logging_only.masks, module: guardrail, tier: P0, hook_point: logging_only, assertions: [masks], exercised_on: [chat_completions, messages], source: "guardrail_hooks/presidio.py", rationale: "Redact in logs without blocking"}
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- {id: guardrail.bedrock.pre_call.blocks, module: guardrail, tier: P0, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/bedrock_guardrails.py", rationale: "AWS content guardrail blocks harmful input"}
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- {id: guardrail.litellm_content_filter.pre_call.blocks, module: guardrail, tier: P0, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "test_team_disable_global_guardrail_e2e.py", rationale: "Local content-filter default-on blocks banned keyword pre-call"}
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- {id: guardrail.litellm_content_filter.pre_call.allows, module: guardrail, tier: P0, hook_point: pre_call, assertions: [allows], exercised_on: [chat_completions], source: "test_team_disable_global_guardrail_e2e.py", rationale: "Team disable_global_guardrails bypasses default-on content filter"}
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- {id: guardrail.litellm_content_filter.apply_endpoint.blocks, module: guardrail, tier: P0, hook_point: apply_endpoint, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_endpoints.py:apply_guardrail", rationale: "POST /guardrails/apply_guardrail blocks banned content for customers that call the apply surface directly"}
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- {id: guardrail.litellm_content_filter.apply_endpoint.allows, module: guardrail, tier: P0, hook_point: apply_endpoint, assertions: [allows], exercised_on: [chat_completions], source: "guardrail_endpoints.py:apply_guardrail", rationale: "POST /guardrails/apply_guardrail returns clean text for allowed input"}
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- {id: guardrail.bedrock.during.blocks, module: guardrail, tier: P0, hook_point: during, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/bedrock_guardrails.py", rationale: "During-call moderation for streaming"}
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- {id: guardrail.bedrock.post_call.blocks, module: guardrail, tier: P0, hook_point: post_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/bedrock_guardrails.py", rationale: "Block harmful output"}
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- {id: guardrail.lakera.pre_call.blocks, module: guardrail, tier: P0, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions, messages], source: "guardrail_hooks/lakera_ai_v2.py", rationale: "Prompt-injection block pre-execution"}
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- {id: guardrail.lakera.post_call.blocks, module: guardrail, tier: P0, hook_point: post_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/lakera_ai_v2.py", rationale: "Post-call injection on multi-turn chains"}
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- {id: guardrail.openai_moderations.pre_call.blocks, module: guardrail, tier: P0, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions, messages], source: "guardrail_hooks/openai/moderations.py", rationale: "Content policy for regulated industries"}
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- {id: guardrail.aim.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions, messages], source: "guardrail_hooks/aim/aim.py", rationale: "Security guardrail malicious-input"}
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- {id: guardrail.aim.post_call.blocks, module: guardrail, tier: P1, hook_point: post_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/aim/aim.py", rationale: "Output security check"}
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- {id: guardrail.ibm_guardrails.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/ibm_guardrails/ibm_detector.py", rationale: "Enterprise multi-policy"}
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- {id: guardrail.ibm_guardrails.post_call.blocks, module: guardrail, tier: P1, hook_point: post_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/ibm_guardrails/ibm_detector.py", rationale: "Output policy validation"}
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- {id: guardrail.semantic_guard.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/semantic_guard", rationale: "Semantic policy compliance"}
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- {id: guardrail.block_code_execution.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/block_code_execution", rationale: "Code-injection prevention"}
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- {id: guardrail.tool_permission.pre_call.allows, module: guardrail, tier: P1, hook_point: pre_call, assertions: [allows], exercised_on: [chat_completions], source: "guardrail_hooks/tool_permission.py", rationale: "Grant allowed tools"}
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- {id: guardrail.tool_permission.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/tool_permission.py", rationale: "Block unauthorized tools"}
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- {id: guardrail.microsoft_purview.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/microsoft_purview/purview_dlp.py", rationale: "DLP sensitive-data disclosure"}
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- {id: guardrail.headroom.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/headroom/headroom.py", rationale: "Anomaly detection threshold"}
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- {id: guardrail.generic_guardrail_api.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/generic_guardrail_api/generic_guardrail_api.py", rationale: "Vendor-agnostic custom API"}
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- {id: guardrail.pangea.pre_call.blocks, module: guardrail, tier: P1, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/pangea/pangea.py", rationale: "API security + DLP"}
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- {id: guardrail.niche_providers.pre_call.blocks, module: guardrail, tier: P2, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: grammar, rationale: "SMOKE cohort: lasso/hiddenlayer/model_armor/qualifire/guardrails_ai/cato/cisco/akto/prompt_security/promptguard/zscaler/vigil/etc"}
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- {id: guardrail.niche_providers.post_call.blocks, module: guardrail, tier: P2, hook_point: post_call, assertions: [blocks], exercised_on: [chat_completions], source: grammar, rationale: "SMOKE niche output filtering"}
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- {id: guardrail.niche_providers.pre_call.allows, module: guardrail, tier: P2, hook_point: pre_call, assertions: [allows], exercised_on: [chat_completions], source: grammar, rationale: "SMOKE niche allow-path passthrough"}
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- {id: guardrail.tool_policy.pre_call.blocks, module: guardrail, tier: P2, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/tool_policy/tool_policy_guardrail.py", rationale: "Tool-use policy enforcement"}
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- {id: guardrail.mcp_security.pre_call.blocks, module: guardrail, tier: P2, hook_point: pre_call, assertions: [blocks], exercised_on: [mcp_operations], source: "guardrail_hooks/mcp_security", rationale: "MCP protocol security"}
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- {id: guardrail.llm_as_a_judge.pre_call.blocks, module: guardrail, tier: P2, hook_point: pre_call, assertions: [blocks], exercised_on: [chat_completions], source: "guardrail_hooks/llm_as_a_judge", rationale: "LLM-based judgment guardrail"}
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- {id: guardrail.litellm_content_filter.pre_mcp_call.blocks, module: guardrail, tier: P1, hook_point: pre_mcp_call, assertions: [blocks], exercised_on: [mcp_operations], source: "guardrail_hooks/litellm_content_filter/content_filter.py:_scan_mcp_tool_call_arguments", rationale: "A general content-filter guardrail configured mode=pre_mcp_call blocks a banned keyword in an MCP tool call's arguments before it reaches the upstream MCP server; a clean argument passes"}
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