procurement-ingest/lambdas/wo/email_processor/tests/test_validation_gate.py

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feat: template-first WO parser + Bedrock fallback, PO Bedrock switch (#99) * Add deterministic template parser for WO emails The workorder-email-processor sends every one of ~22.9k emails/month to an LLM, but ~93.6% are the plain-text "AMAZON UPDATE WO DETAILS" comment template and ~6.4% the HTML "AMAZON assign Work Order" template. Parse those two shapes deterministically, offline, so the AI call is reserved for the long tail. The module is pure (no boto3, no network). try_deterministic_parse classifies by subject, extracts the shared contract fields, and returns a result ONLY when it passes a strict fail-closed validation gate: exact contract-key set, subject/id agreement, the literal "Work Order: <id>" double space, per-type required fields, site-code shape, and a label-bleed guard so a value that over-ran into the next field fails. Any miss, drift, or extractor exception yields None so the caller falls back to the AI extractor -- data is never corrupted, only the fallback rate rises. Refs: #23 * Migrate WO processor to Bedrock and fix comment_id collision Switch the AI path from the Anthropic SDK to bedrock-runtime InvokeModel on the inference profile us.anthropic.claude-haiku-4-5-20251001-v1:0 (BEDROCK_MODEL_ID env), so parsing no longer needs a provider API key or Secrets Manager secret. The EXTRACTION_PROMPT and JSON contract are kept byte-identical, so the AI-fallback output is unchanged. Try the new deterministic template parser first and only call Bedrock on a miss/invalid result. Fix issue #23: the WorkOrderComments range key was work_order_id#<comment_time>, so two emails on one WO with an identical or absent comment time collided and overwrote each other. Derive a 12-hex suffix from the S3 object key alone -- deterministic, so an async retry of the same object is byte-identical (idempotent) while distinct emails get distinct keys -- and keep wall-clock now() out of the key (literal 'nocomment' segment when comment_time is absent). Also emit one CloudWatch EMF line per record (Seahaven/WorkorderIngest ParseOutcome, dimensioned by ParseMethod/TemplateId) for parse-outcome observability, replace the deprecated datetime.utcnow() with datetime.now(timezone.utc), and drop the anthropic dependency. Refs: #23 * Migrate PO processor to Bedrock Switch the PO email processor's AI extraction from the Anthropic SDK to bedrock-runtime InvokeModel on the inference profile us.anthropic.claude-haiku-4-5-20251001-v1:0 (BEDROCK_MODEL_ID env), so it no longer needs a provider API key or Secrets Manager secret. PO parsing stays fully AI -- only the provider changes. The EXTRACTION_PROMPT is kept byte-identical and the Bedrock text output is still decoded with json.loads(..., parse_float=Decimal), which DynamoDB requires (it rejects floats). Replace the deprecated datetime.utcnow() with datetime.now(timezone.utc) and drop the anthropic dependency. * Grant Bedrock IAM, drop Anthropic secrets, add fallback alarm Both stacks moved their processors from the Anthropic API to the Bedrock inference profile us.anthropic.claude-haiku-4-5-20251001-v1:0. Grant each processor role bedrock:InvokeModel + bedrock:InvokeModelWithResponseStream on BOTH the inference-profile ARN AND the per-region foundation-model ARNs for us-east-1/us-east-2/us-west-2 (empty-account) -- the us.* profile routes cross-region, so a profile-only grant AccessDenies at runtime. Remove both anthropic-api-key Secret constructs, their grant_read, and the ANTHROPIC_API_KEY_SECRET_ARN env; add BEDROCK_MODEL_ID. The secrets had RemovalPolicy.RETAIN so they are orphaned, not deleted -- flagged in the README for manual post-deploy deletion and key revocation. Add the workorder-email-processor-template-fallback-rate alarm: a FILL(0) + >=10-sample volume-floor MathExpression over the EMF ParseOutcome metric (15-min periods) that pages when the AI-fallback share exceeds 15% sustained, catching Hexagon template drift. ALARM-only SnsAction to site-alerts, no OK action, NOT_BREACHING, matching the existing stack idiom. * Add offline WO parser test suite Cover the deterministic parser with golden-file tests over 55 real scrubbed .eml fixtures (both comment sub-shapes, username Submitted-By, address present/absent, br+CRLF assign addresses), fail-closed validation-gate rules, adversarial and prompt-injection cases that must route to ai_fallback or parse without corrupting other fields, the issue #23 comment_id idempotency invariants, and the Bedrock-fallback dispatch plus EMF-metric emission with a mocked invoke_model. Extend pytest.ini testpaths to discover the co-located suite, and update tests/conftest.load_handler to put a handler's own directory on sys.path so the WO handler's new `from template_parser import ...` resolves under the existing shared handler tests. Point test_local.py at the new template-first + Bedrock flow. Refs: #23 * Document Bedrock migration and WO parse flow in README Record the provider switch to the Bedrock inference profile (no Anthropic API key or Secrets Manager secret, with the retired secrets flagged for manual deletion), the WO deterministic-template-first + AI-fallback flow, the new ParseOutcome EMF metric and template-fallback-rate alarm, the issue #23 comment_id format change, the +00:00 aware-UTC timestamp shift, and offline test instructions. Refs: #23 * Fix f-string lint and formatting in backfill scripts Drop the f prefix from two f-strings that carry no placeholders (F541) and apply ruff format, so `ruff check` / `ruff format --check` pass in CI. * Emit ParseMethod-only EMF set so fallback alarm can fire The fallback-rate alarm queries the ParseOutcome series keyed on ParseMethod alone, but the emitter published only the joint (ParseMethod, TemplateId) dimension set. CloudWatch materializes exactly the listed dimension sets and does not auto-aggregate, so the alarm's series never received data: it evaluated a constant 0 and could never page on template-drift coverage collapse. Publish both ["ParseMethod"] and ["ParseMethod","TemplateId"] and update the EMF regression test to assert both sets are present. * Commit WO parser .eml fixtures for executable coverage The parser test suite globbed for input .eml fixtures that the repo's `*.eml` ignore rule kept uncommitted, so every parametrized golden and fail-closed test collected zero cases and CI could not exercise the deterministic parser that handles 100% of WO email volume. Add a fixtures-only negation to .gitignore and commit the 55 scrubbed positive samples (50 update-plaintext, 5 assign-html) plus 14 ai-fallback and 3 adversarial fixtures. The ai-fallback set covers each fail-closed reason code (subject_no_match, single_space_work_order, malformed_site_code, label_bleed, creation_time_unparseable, wo_id_mismatch, missing_required_field) and the adversarial set proves the parser is total and confines prompt-injection payloads to comment_text without steering the structured fields. * Fix WO parser advisories A1-A3 (PR #99 follow-ups) A1 — AI-fallback comment_id nondeterminism: parsed comment_time is model output and not stable across Lambda async retries, so on the ai_fallback path the comment_id range-key time segment now derives from the email Date header (deterministic per S3 object) instead of the model's comment_time. The template path is unchanged (its comment_time is a pure function of the raw email). Bedrock invoke pins temperature 0 so retries reproduce the same extraction. Closes the #23 reopening on the AI path. A2 — EMF record now carries the spec-required _aws.Timestamp (epoch ms) so CloudWatch reliably extracts the ParseOutcome datapoint that the fallback-rate alarm depends on. A3 — T1 New Comment capture no longer truncates at the first blank line; multi-paragraph comments are captured through internal blanks and terminate at the next label/separator. 17 golden files regenerated from the real fixtures accordingly. Hardening from the sh-security-review pass on this diff: - _header_date_iso is total: OverflowError/OSError from an extreme Date header fall back to 'nocomment' instead of failing the invocation. - _capture_block trims blanks in O(n) (no pop(0)) — removes a quadratic path on a crafted large blank run. - work_order_id is enforced digits-only on BOTH parse paths before it is used as a DynamoDB key, so prompt-injected AI output cannot forge '#' range-key segments or land on an arbitrary WO.
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"""Fail-closed validation-gate tests.
Each known drift / adversarial shape must be rejected with the expected reason
code, and direct unit tests exercise each individual gate rule.
"""
import pytest
from _wo_parser_support import load_email
from template_parser import (
CONTRACT_KEYS,
extract_update_plaintext,
try_deterministic_parse,
validate,
fix: add fail-closed validation gate and XML-delimited prompt on ai_fallback path (#104) * fix: add fail-closed validation gate and XML-delimited prompt on ai_fallback path The ai_fallback parse path applied no validation gate to raw Bedrock/LLM output before DynamoDB writes, and the extraction prompt concatenated the untrusted email body directly with no instructions-vs-data delimiter. A DKIM-passing attacker could prompt-inject arbitrary field values into the work-order store. Changes: - wrap untrusted email in \<email\> XML block with prompt instructing the model to treat its contents as data only - add validate_ai_fallback() in template_parser that enforces the same contract keys, enums, and patterns as the template path before any write - call validate_ai_fallback() in handler() dispatch; emit an ai_fallback_rejected EMF metric on failure and skip the record - add 17 unit tests covering every gate rule and two end-to-end dispatch tests (injected email_type, injected status) Refs #101 * style: apply ruff formatting to fix CI check * harden ai_fallback gate: review fixes + security-review findings Review follow-up on the ai_fallback validation gate (PR #104), plus findings from a fan-out /sh-security-review of the change surface. Reviewer FIX items: - Neutralize forged <email> delimiters in the untrusted body before wrapping, so an in-body </email> cannot escape the data block. - Fail closed on non-dict model output instead of crashing the handler into async retries; count ai_fallback_rejected parses in the fallback-rate alarm and add a dedicated rejected-parse alarm so a gate-rejection drift outage is not silent. - Return a distinct invalid_status reason (was malformed_site_code); validate ISO-8601 dates; README + docstring updates. Security-review findings (detector fan-out + proof-or-kill verifier): - ReDoS (confirmed, medium): the tag neutralizer used two \s* around an optional /, backtracking quadratically on "<" + a long whitespace run (~32s at 100k chars -- one email could time out the Lambda). Collapse to a single [\s/]* class: linear, same defanging. - Unhashable-type crash (confirmed): a JSON list/dict for email_type or status made `x in <set>` raise TypeError, escaping the gate into retries. Guard with isinstance(str) before membership. - Unicode/newline regex (confirmed): _WO_ID_RE/_SITE_CODE_RE used ^..$ with \d, admitting fullwidth digits ("12345" as a lookalike partition key) and trailing newlines. Switch to \A[0-9]+\Z (and the handler's inline recheck to [0-9]) so neither passes. - Alarm comment (confirmed, low): corrected the "slow trickle still pages" wording -- rejections >~25-30 min apart page on neither alarm, the same knowingly-accepted residual as sender-auth-rejected. Refuted: residual free-text prompt injection is inherent to trusting allowlisted senders, not a new primitive; no DynamoDB key-poisoning bypass survives both gates ('#' can never enter work_order_id). 7 new regression tests. All 260 tests pass; ruff clean; cdk synth OK. --------- Co-authored-by: amoussa1229 <166072409+amoussa1229@users.noreply.github.com> Co-authored-by: Adam Moussa <adam@seahavenind.com>
2026-07-16 16:23:28 -04:00
validate_ai_fallback,
feat: template-first WO parser + Bedrock fallback, PO Bedrock switch (#99) * Add deterministic template parser for WO emails The workorder-email-processor sends every one of ~22.9k emails/month to an LLM, but ~93.6% are the plain-text "AMAZON UPDATE WO DETAILS" comment template and ~6.4% the HTML "AMAZON assign Work Order" template. Parse those two shapes deterministically, offline, so the AI call is reserved for the long tail. The module is pure (no boto3, no network). try_deterministic_parse classifies by subject, extracts the shared contract fields, and returns a result ONLY when it passes a strict fail-closed validation gate: exact contract-key set, subject/id agreement, the literal "Work Order: <id>" double space, per-type required fields, site-code shape, and a label-bleed guard so a value that over-ran into the next field fails. Any miss, drift, or extractor exception yields None so the caller falls back to the AI extractor -- data is never corrupted, only the fallback rate rises. Refs: #23 * Migrate WO processor to Bedrock and fix comment_id collision Switch the AI path from the Anthropic SDK to bedrock-runtime InvokeModel on the inference profile us.anthropic.claude-haiku-4-5-20251001-v1:0 (BEDROCK_MODEL_ID env), so parsing no longer needs a provider API key or Secrets Manager secret. The EXTRACTION_PROMPT and JSON contract are kept byte-identical, so the AI-fallback output is unchanged. Try the new deterministic template parser first and only call Bedrock on a miss/invalid result. Fix issue #23: the WorkOrderComments range key was work_order_id#<comment_time>, so two emails on one WO with an identical or absent comment time collided and overwrote each other. Derive a 12-hex suffix from the S3 object key alone -- deterministic, so an async retry of the same object is byte-identical (idempotent) while distinct emails get distinct keys -- and keep wall-clock now() out of the key (literal 'nocomment' segment when comment_time is absent). Also emit one CloudWatch EMF line per record (Seahaven/WorkorderIngest ParseOutcome, dimensioned by ParseMethod/TemplateId) for parse-outcome observability, replace the deprecated datetime.utcnow() with datetime.now(timezone.utc), and drop the anthropic dependency. Refs: #23 * Migrate PO processor to Bedrock Switch the PO email processor's AI extraction from the Anthropic SDK to bedrock-runtime InvokeModel on the inference profile us.anthropic.claude-haiku-4-5-20251001-v1:0 (BEDROCK_MODEL_ID env), so it no longer needs a provider API key or Secrets Manager secret. PO parsing stays fully AI -- only the provider changes. The EXTRACTION_PROMPT is kept byte-identical and the Bedrock text output is still decoded with json.loads(..., parse_float=Decimal), which DynamoDB requires (it rejects floats). Replace the deprecated datetime.utcnow() with datetime.now(timezone.utc) and drop the anthropic dependency. * Grant Bedrock IAM, drop Anthropic secrets, add fallback alarm Both stacks moved their processors from the Anthropic API to the Bedrock inference profile us.anthropic.claude-haiku-4-5-20251001-v1:0. Grant each processor role bedrock:InvokeModel + bedrock:InvokeModelWithResponseStream on BOTH the inference-profile ARN AND the per-region foundation-model ARNs for us-east-1/us-east-2/us-west-2 (empty-account) -- the us.* profile routes cross-region, so a profile-only grant AccessDenies at runtime. Remove both anthropic-api-key Secret constructs, their grant_read, and the ANTHROPIC_API_KEY_SECRET_ARN env; add BEDROCK_MODEL_ID. The secrets had RemovalPolicy.RETAIN so they are orphaned, not deleted -- flagged in the README for manual post-deploy deletion and key revocation. Add the workorder-email-processor-template-fallback-rate alarm: a FILL(0) + >=10-sample volume-floor MathExpression over the EMF ParseOutcome metric (15-min periods) that pages when the AI-fallback share exceeds 15% sustained, catching Hexagon template drift. ALARM-only SnsAction to site-alerts, no OK action, NOT_BREACHING, matching the existing stack idiom. * Add offline WO parser test suite Cover the deterministic parser with golden-file tests over 55 real scrubbed .eml fixtures (both comment sub-shapes, username Submitted-By, address present/absent, br+CRLF assign addresses), fail-closed validation-gate rules, adversarial and prompt-injection cases that must route to ai_fallback or parse without corrupting other fields, the issue #23 comment_id idempotency invariants, and the Bedrock-fallback dispatch plus EMF-metric emission with a mocked invoke_model. Extend pytest.ini testpaths to discover the co-located suite, and update tests/conftest.load_handler to put a handler's own directory on sys.path so the WO handler's new `from template_parser import ...` resolves under the existing shared handler tests. Point test_local.py at the new template-first + Bedrock flow. Refs: #23 * Document Bedrock migration and WO parse flow in README Record the provider switch to the Bedrock inference profile (no Anthropic API key or Secrets Manager secret, with the retired secrets flagged for manual deletion), the WO deterministic-template-first + AI-fallback flow, the new ParseOutcome EMF metric and template-fallback-rate alarm, the issue #23 comment_id format change, the +00:00 aware-UTC timestamp shift, and offline test instructions. Refs: #23 * Fix f-string lint and formatting in backfill scripts Drop the f prefix from two f-strings that carry no placeholders (F541) and apply ruff format, so `ruff check` / `ruff format --check` pass in CI. * Emit ParseMethod-only EMF set so fallback alarm can fire The fallback-rate alarm queries the ParseOutcome series keyed on ParseMethod alone, but the emitter published only the joint (ParseMethod, TemplateId) dimension set. CloudWatch materializes exactly the listed dimension sets and does not auto-aggregate, so the alarm's series never received data: it evaluated a constant 0 and could never page on template-drift coverage collapse. Publish both ["ParseMethod"] and ["ParseMethod","TemplateId"] and update the EMF regression test to assert both sets are present. * Commit WO parser .eml fixtures for executable coverage The parser test suite globbed for input .eml fixtures that the repo's `*.eml` ignore rule kept uncommitted, so every parametrized golden and fail-closed test collected zero cases and CI could not exercise the deterministic parser that handles 100% of WO email volume. Add a fixtures-only negation to .gitignore and commit the 55 scrubbed positive samples (50 update-plaintext, 5 assign-html) plus 14 ai-fallback and 3 adversarial fixtures. The ai-fallback set covers each fail-closed reason code (subject_no_match, single_space_work_order, malformed_site_code, label_bleed, creation_time_unparseable, wo_id_mismatch, missing_required_field) and the adversarial set proves the parser is total and confines prompt-injection payloads to comment_text without steering the structured fields. * Fix WO parser advisories A1-A3 (PR #99 follow-ups) A1 — AI-fallback comment_id nondeterminism: parsed comment_time is model output and not stable across Lambda async retries, so on the ai_fallback path the comment_id range-key time segment now derives from the email Date header (deterministic per S3 object) instead of the model's comment_time. The template path is unchanged (its comment_time is a pure function of the raw email). Bedrock invoke pins temperature 0 so retries reproduce the same extraction. Closes the #23 reopening on the AI path. A2 — EMF record now carries the spec-required _aws.Timestamp (epoch ms) so CloudWatch reliably extracts the ParseOutcome datapoint that the fallback-rate alarm depends on. A3 — T1 New Comment capture no longer truncates at the first blank line; multi-paragraph comments are captured through internal blanks and terminate at the next label/separator. 17 golden files regenerated from the real fixtures accordingly. Hardening from the sh-security-review pass on this diff: - _header_date_iso is total: OverflowError/OSError from an extreme Date header fall back to 'nocomment' instead of failing the invocation. - _capture_block trims blanks in O(n) (no pop(0)) — removes a quadratic path on a crafted large blank run. - work_order_id is enforced digits-only on BOTH parse paths before it is used as a DynamoDB key, so prompt-injected AI output cannot forge '#' range-key segments or land on an arbitrary WO.
2026-07-16 12:45:11 -04:00
)
# Fixture stem -> expected fail-closed reason code.
EXPECTED_REASONS = {
"unknown-subject": "subject_no_match",
"missing-id": "subject_no_match",
"single-space-work-order": "single_space_work_order",
"nondigit-id": "missing_required_field",
"empty-new-comment": "missing_required_field",
"malformed-site-code": "malformed_site_code",
"label-bleed-comment": "label_bleed",
"unparseable-creation-time": "creation_time_unparseable",
"cancellation-t1": "missing_required_field",
"update-status-t1": "missing_required_field",
"missing-building-and-comment": "missing_required_field",
"t2-wo-id-mismatch": "wo_id_mismatch",
"t2-missing-address": "missing_required_field",
"t2-unparseable-date": "creation_time_unparseable",
}
@pytest.mark.parametrize("stem,reason", sorted(EXPECTED_REASONS.items()))
def test_gate_rejects_with_reason(stem, reason):
parsed, method, _tid, got_reason = try_deterministic_parse(
load_email("ai-fallback", stem)
)
assert parsed is None
assert method == "ai_fallback"
assert got_reason == reason
# --- Direct unit tests on validate() ---
def _good_t1_email():
return load_email("update-plaintext", "update-plaintext-01")
def _good_candidate():
return extract_update_plaintext(_good_t1_email())
def test_baseline_candidate_is_valid():
ok, reason = validate(_good_candidate(), "update_plaintext", _good_t1_email())
assert ok and reason == "ok"
def test_rule1_unknown_template():
ok, reason = validate(_good_candidate(), "unknown", _good_t1_email())
assert not ok and reason == "subject_no_match"
def test_rule2_extra_key_fails():
cand = _good_candidate()
cand["surprise"] = "x"
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "key_set_mismatch"
def test_rule2_missing_key_fails():
cand = _good_candidate()
del cand["address"]
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "key_set_mismatch"
def test_rule3_nondigit_wo():
cand = _good_candidate()
cand["work_order_id"] = "12A45"
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "missing_required_field"
def test_rule5_wrong_email_type():
cand = _good_candidate()
cand["email_type"] = "new_work_order" # wrong for a T1 template
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "email_type_mismatch"
def test_rule6_bad_site_code():
cand = _good_candidate()
cand["site_code"] = "workshop"
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "malformed_site_code"
def test_rule7_bad_status():
cand = _good_candidate()
cand["status"] = "frobnicated"
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "malformed_site_code"
def test_rule8_empty_comment_text():
cand = _good_candidate()
cand["comment_text"] = " "
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "missing_required_field"
def test_rule9_label_bleed_in_comment():
cand = _good_candidate()
cand["comment_text"] = "text that leaked Building: WCO0 into the value"
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "label_bleed"
def test_rule9_separator_bleed_in_address():
cand = _good_candidate()
cand["address"] = "123 Main St ________________"
ok, reason = validate(cand, "update_plaintext", _good_t1_email())
assert not ok and reason == "label_bleed"
def test_contract_keys_match_extraction_prompt():
"""The parser's key set must be exactly the AI EXTRACTION_PROMPT contract, so
the deterministic and AI-fallback paths write identical shapes downstream."""
import re
import handler
# The prompt's JSON skeleton uses union-type pseudo-values (not strict JSON)
# and repeats some enum terms in prose bullets, so pull quoted "key": tokens
# and assert every contract key is a field the AI is asked to emit (the
# parser must never invent a key outside the AI contract).
prompt_keys = set(re.findall(r'"([a-z_]+)":', handler.EXTRACTION_PROMPT))
assert set(CONTRACT_KEYS).issubset(prompt_keys)
assert len(CONTRACT_KEYS) == 16 # current EXTRACTION_PROMPT field count
fix: add fail-closed validation gate and XML-delimited prompt on ai_fallback path (#104) * fix: add fail-closed validation gate and XML-delimited prompt on ai_fallback path The ai_fallback parse path applied no validation gate to raw Bedrock/LLM output before DynamoDB writes, and the extraction prompt concatenated the untrusted email body directly with no instructions-vs-data delimiter. A DKIM-passing attacker could prompt-inject arbitrary field values into the work-order store. Changes: - wrap untrusted email in \<email\> XML block with prompt instructing the model to treat its contents as data only - add validate_ai_fallback() in template_parser that enforces the same contract keys, enums, and patterns as the template path before any write - call validate_ai_fallback() in handler() dispatch; emit an ai_fallback_rejected EMF metric on failure and skip the record - add 17 unit tests covering every gate rule and two end-to-end dispatch tests (injected email_type, injected status) Refs #101 * style: apply ruff formatting to fix CI check * harden ai_fallback gate: review fixes + security-review findings Review follow-up on the ai_fallback validation gate (PR #104), plus findings from a fan-out /sh-security-review of the change surface. Reviewer FIX items: - Neutralize forged <email> delimiters in the untrusted body before wrapping, so an in-body </email> cannot escape the data block. - Fail closed on non-dict model output instead of crashing the handler into async retries; count ai_fallback_rejected parses in the fallback-rate alarm and add a dedicated rejected-parse alarm so a gate-rejection drift outage is not silent. - Return a distinct invalid_status reason (was malformed_site_code); validate ISO-8601 dates; README + docstring updates. Security-review findings (detector fan-out + proof-or-kill verifier): - ReDoS (confirmed, medium): the tag neutralizer used two \s* around an optional /, backtracking quadratically on "<" + a long whitespace run (~32s at 100k chars -- one email could time out the Lambda). Collapse to a single [\s/]* class: linear, same defanging. - Unhashable-type crash (confirmed): a JSON list/dict for email_type or status made `x in <set>` raise TypeError, escaping the gate into retries. Guard with isinstance(str) before membership. - Unicode/newline regex (confirmed): _WO_ID_RE/_SITE_CODE_RE used ^..$ with \d, admitting fullwidth digits ("12345" as a lookalike partition key) and trailing newlines. Switch to \A[0-9]+\Z (and the handler's inline recheck to [0-9]) so neither passes. - Alarm comment (confirmed, low): corrected the "slow trickle still pages" wording -- rejections >~25-30 min apart page on neither alarm, the same knowingly-accepted residual as sender-auth-rejected. Refuted: residual free-text prompt injection is inherent to trusting allowlisted senders, not a new primitive; no DynamoDB key-poisoning bypass survives both gates ('#' can never enter work_order_id). 7 new regression tests. All 260 tests pass; ruff clean; cdk synth OK. --------- Co-authored-by: amoussa1229 <166072409+amoussa1229@users.noreply.github.com> Co-authored-by: Adam Moussa <adam@seahavenind.com>
2026-07-16 16:23:28 -04:00
# --- validate_ai_fallback unit tests -----------------------------------------
def _ai_candidate():
return {k: None for k in CONTRACT_KEYS}
def test_ai_fallback_baseline_is_valid():
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
ok, reason = validate_ai_fallback(cand)
assert ok and reason == "ok"
def test_ai_fallback_nondigit_wo_id():
cand = _ai_candidate()
cand["work_order_id"] = "12A45"
cand["email_type"] = "update"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "missing_required_field"
def test_ai_fallback_null_wo_id():
cand = _ai_candidate()
cand["work_order_id"] = None
cand["email_type"] = "update"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "missing_required_field"
def test_ai_fallback_hash_in_wo_id():
cand = _ai_candidate()
cand["work_order_id"] = "123#spoofed#deadbeef"
cand["email_type"] = "update"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "missing_required_field"
def test_ai_fallback_invalid_email_type():
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "exploit"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "missing_required_field"
def test_ai_fallback_null_email_type():
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = None
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "missing_required_field"
def test_ai_fallback_bad_status_enum():
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["status"] = "frobnicated"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "invalid_status"
def test_ai_fallback_unhashable_enum_fails_closed():
# A JSON list/dict for an enum field is unhashable; the gate must fail
# closed (isinstance guard), not raise TypeError into async retries.
for field, bad in (
("email_type", ["update"]),
("email_type", {"x": 1}),
("status", ["new"]),
("status", {"x": 1}),
):
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand[field] = bad
ok, reason = validate_ai_fallback(cand) # must not raise
assert not ok, f"{field}={bad!r} should fail closed"
def test_ai_fallback_fullwidth_digit_wo_id_rejected():
# Fullwidth digits render like ASCII but are a distinct partition key;
# [0-9] (not \d) must reject them.
cand = _ai_candidate()
cand["work_order_id"] = "12345" # "12345" fullwidth
cand["email_type"] = "update"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "missing_required_field"
def test_ai_fallback_trailing_newline_rejected():
# \A..\Z (not ^..$) must reject a trailing newline in wo_id and site_code.
cand = _ai_candidate()
cand["work_order_id"] = "12345\n"
cand["email_type"] = "update"
ok, _ = validate_ai_fallback(cand)
assert not ok
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["site_code"] = "WIL1\n"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "malformed_site_code"
def test_ai_fallback_non_dict_fails_closed():
# json.loads on model output can yield any JSON type; the gate must fail
# closed on a non-object rather than raise into async retries / DLQ.
for bad in ([], "string", 42, None, [{"work_order_id": "12345"}]):
ok, reason = validate_ai_fallback(bad)
assert not ok and reason == "not_an_object", f"{bad!r} should fail closed"
def test_ai_fallback_unparseable_sentinel_rejected():
# The template parser's internal _UNPARSEABLE sentinel must never survive
# the AI gate into the store.
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["comment_time"] = "__UNPARSEABLE__"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "creation_time_unparseable"
def test_ai_fallback_non_iso_date_rejected():
for key in ("date_reported", "scheduled_start", "due_date", "comment_time"):
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand[key] = "ignore previous instructions"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "creation_time_unparseable", f"{key} not gated"
def test_ai_fallback_iso_dates_accepted():
for value in ("2026-07-16", "2026-07-16T10:15:00", "2026-07-16T10:15:00Z", None):
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["date_reported"] = value
cand["comment_time"] = value
ok, reason = validate_ai_fallback(cand)
assert ok, f"date {value!r} should pass"
def test_ai_fallback_valid_status_ok():
for status in (
"new",
"assigned",
"in_progress",
"on_hold",
"completed",
"cancelled",
"unknown",
None,
):
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["status"] = status
ok, reason = validate_ai_fallback(cand)
assert ok, f"status={status} should pass"
def test_ai_fallback_bad_site_code():
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["site_code"] = "workshop"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "malformed_site_code"
def test_ai_fallback_valid_site_codes():
for code in ("WIL1", "ZDL8", "AB12", None):
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["site_code"] = code
ok, reason = validate_ai_fallback(cand)
assert ok, f"site_code={code} should pass"
def test_ai_fallback_key_set_mismatch_extra():
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
cand["surprise"] = "x"
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "key_set_mismatch"
def test_ai_fallback_key_set_mismatch_missing():
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = "update"
del cand["address"]
ok, reason = validate_ai_fallback(cand)
assert not ok and reason == "key_set_mismatch"
def test_ai_fallback_all_valid_email_types():
for et in ("new_work_order", "update", "comment", "cancellation"):
cand = _ai_candidate()
cand["work_order_id"] = "12345"
cand["email_type"] = et
ok, reason = validate_ai_fallback(cand)
assert ok, f"email_type={et} should pass"