Files
turnstone/tests/test_output_assessment_storage.py
T
Patrick Buckley 3233719856 feat(judge): output_guard LLM stage with capability gate (#560 mitigation #1)
Adds a second, LLM-driven stage to the output guard so domain-camouflaged
prompt-injection payloads that the regex stage misses (arXiv:2605.22001 —
Llama 3.1 8B evades the existing regex set on ~90% of camouflaged
prompts) get caught before the tool output lands in the assistant's
context.

## Surface

* New `OutputGuardJudge` in `turnstone/core/output_guard_judge.py` —
  synchronous, single-shot LLM call.  Inlines the alias-resolution +
  client-config + JSON-parsing helpers (copied verbatim from
  `IntentJudge` at `judge.py:917-969` / `1604-1659`) rather than going
  through a shared module — when `IntentJudge` lifts its own helpers,
  both copies move together.

* JSON-in-content verdict with a 3-strategy parser (direct / markdown
  fence / balanced braces).  `IntentJudge` ships a 4th regex-field
  fallback; OutputGuardJudge deliberately doesn't, because strategy-4
  hits on broken LLM output can extract a "verdict" from the model's
  reasoning quote that lands in storage looking identical to a clean
  strategy-1 result.  Failure of all three returns
  `error="unparseable_verdict"` and the heuristic stage stands.

* `OutputJudgeVerdict` is a frozen dataclass with:
  `risk_level` (none/low/medium/high — normalises `critical`→`high`
  and `info[rmational]`→`low` for IntentJudge-echo safety),
  `flags: tuple[str, ...]`, `reasoning`, `confidence: float`
  (0.0-1.0, parsed + clamped from the LLM's self-report;
  pass-through to audit, no threshold gating), `judge_model`,
  `latency_ms`, `error`.

* Real wall-clock timeout via `ThreadPoolExecutor.shutdown(wait=False,
  cancel_futures=True)` on the timeout/cancel path — `with ... as ex:`
  would block return until the worker drained.  1s `cancel_event`
  poll mirrors `IntentJudge._run_judge` at `judge.py:1117-1118`.

* HTTP client lazy-init + reuse for the judge instance's lifetime.
  Session-side model swap drops the entire judge, dropping the client
  with it.

* Untrusted tool output wrapped in per-call random-nonced
  `<tool_output_NONCE>...</tool_output_NONCE>` fence.  Closing-tag
  substrings in the raw text are case-insensitively backslash-escaped
  first (`</tool_output` → `<\/tool_output`) so an attacker can't
  break out even if they guess the nonce.  System prompt classifies
  the fenced region as UNTRUSTED DATA so directives inside are
  evaluated as content, not obeyed.

* Judge user prompt carries the heuristic verdict (risk + flags +
  annotations), the tool description (looked up from the session's
  tools registry), and the tool args (truncated to 500 chars, also
  classified UNTRUSTED in the system prompt since they may be
  caller-supplied).  Lets the judge defer to the regex on credential
  leaks and focus on injection signals the regex set misses; also
  enables output-vs-request plausibility reasoning.

## Session integration

* `_evaluate_output(call_id, output, func_name, *, tool_args="")` —
  heuristic always runs; LLM stage runs when `judge.output_guard_llm`
  is enabled.  When the LLM produces a usable verdict and the
  heuristic didn't detect credentials, the LLM verdict is acted on;
  otherwise the heuristic stands.

* Credential redaction is a regex-only signal.  When `heuristic.
  sanitized` is non-None, the heuristic owns the acted assessment
  regardless of what the LLM said — an LLM asked about prompt-
  injection can correctly label a credential-bearing output as
  "none" risk for injection, but the secret still needs redaction.

* `_batch_evaluate_outputs` runs the per-tool guard concurrently
  (4-worker pool) when LLM is enabled and there are ≥2 string
  outputs — collapses N×LLM-latency to ⌈N/4⌉×latency on the common
  5-20 tool-calls-per-turn turn.

* Per-session `TokenBucket(rate=1.0, burst=60)` caps adversarial
  LLM-fan-out cost at 60 calls/min/session.

* Pre-truncation: the per-tool loop truncates output before the
  judge sees it, so the judge evaluates exactly what enters the
  assistant's context (no wasted tokens on text that won't land).

* Both heuristic and LLM tier rows persisted to `output_assessments`
  when the LLM ran (audit completeness); heuristic-only rows skip
  when matched-clean to keep the table focused.

## Storage

Migration 057 extends `output_assessments` with five LLM-tier
columns: `tier` (`heuristic` / `llm`, backfilled to `heuristic`),
`reasoning`, `judge_model`, `latency_ms`, `confidence`.  Tie-break
on `(created DESC, tier='llm' first)` so downstream consumers see
the acted verdict first when the two rows tie at second resolution.

`StorageBackend.record_output_assessment` + sqlite/pg implementations
+ `SessionUIBase.record_output_assessment` + `SessionUI` protocol +
the test stub overrides (cli, eval, 9 test files) all take the new
LLM-tier kwargs.

## Config surface

Three new judge.* settings in `settings_registry`:

* `judge.output_guard_llm` (bool, default False) — capability gate.
  Default off; operators opt in once a small/fast model is pointed
  at `output_guard_model`.

* `judge.output_guard_model` (str, default "") — alias for the LLM
  stage.  Empty inherits the session model (same fallback shape as
  `judge.model`).

* `judge.output_guard_llm_timeout` (float, default 30.0, min 1.0) —
  wall-clock budget per call.

Both `server.py` and `console/session_factory.py` wire these into
the `JudgeConfig` they hand to `ChatSession`.

## Notes

* No backwards-compatibility shims — the LLM stage is purely additive.

* No reasoning/threshold gating on confidence; it rides as an
  audit-only signal per maintainer direction.  Surface it in the
  `on_output_warning` dict so live UI / cluster broadcast can sort
  flagged outputs by judge certainty.

* Tests: 392 lines of judge-only coverage (`test_output_guard_judge.
  py`) + 629 lines of session-integration coverage in `test_session.
  py`, plus the storage and stub-shape updates.
2026-05-24 17:49:27 -07:00

181 lines
7.6 KiB
Python

"""Tests for output assessment storage operations."""
from __future__ import annotations
from datetime import UTC, datetime, timedelta
def _make_assessment_kwargs(**overrides):
"""Build default kwargs for record_output_assessment."""
defaults = {
"assessment_id": "oa_001",
"ws_id": "ws-abc",
"call_id": "tc_001",
"func_name": "bash",
"flags": '["credential_leak"]',
"risk_level": "high",
"annotations": "[]",
"output_length": 256,
"redacted": False,
}
defaults.update(overrides)
return defaults
# ---------------------------------------------------------------------------
# CRUD Operations
# ---------------------------------------------------------------------------
class TestOutputAssessmentCRUD:
def test_record_and_list(self, db):
db.record_output_assessment(**_make_assessment_kwargs())
results = db.list_output_assessments()
assert len(results) == 1
assert results[0]["assessment_id"] == "oa_001"
assert results[0]["ws_id"] == "ws-abc"
assert results[0]["func_name"] == "bash"
assert results[0]["risk_level"] == "high"
# ---------------------------------------------------------------------------
# Count queries
# ---------------------------------------------------------------------------
class TestOutputAssessmentCount:
def test_count_basic(self, db):
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa1"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa2"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa3"))
assert db.count_output_assessments() == 3
def test_count_empty(self, db):
assert db.count_output_assessments() == 0
def test_count_with_ws_id(self, db):
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa1", ws_id="ws-1"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa2", ws_id="ws-1"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa3", ws_id="ws-2"))
assert db.count_output_assessments(ws_id="ws-1") == 2
def test_count_with_risk_level(self, db):
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa1", risk_level="low")
)
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa2", risk_level="high")
)
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa3", risk_level="high")
)
assert db.count_output_assessments(risk_level="high") == 2
def test_count_with_since(self, db):
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa1"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa2"))
future = (datetime.now(UTC) + timedelta(minutes=5)).strftime("%Y-%m-%dT%H:%M:%S")
assert db.count_output_assessments(since=future) == 0
def test_count_with_until(self, db):
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa1"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa2"))
past = "2020-01-01T00:00:00"
assert db.count_output_assessments(until=past) == 0
def test_count_with_date_range(self, db):
now = datetime.now(UTC)
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa1"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa2"))
db.record_output_assessment(**_make_assessment_kwargs(assessment_id="oa3"))
one_minute_ago = (now - timedelta(minutes=1)).strftime("%Y-%m-%dT%H:%M:%S")
one_minute_later = (now + timedelta(minutes=1)).strftime("%Y-%m-%dT%H:%M:%S")
assert db.count_output_assessments(since=one_minute_ago, until=one_minute_later) == 3
def test_count_matches_list_length(self, db):
"""Count with filters matches the length of list with same filters."""
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa1", ws_id="ws-1", risk_level="high")
)
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa2", ws_id="ws-1", risk_level="low")
)
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa3", ws_id="ws-2", risk_level="high")
)
now = datetime.now(UTC)
one_minute_ago = (now - timedelta(minutes=1)).strftime("%Y-%m-%dT%H:%M:%S")
one_minute_later = (now + timedelta(minutes=1)).strftime("%Y-%m-%dT%H:%M:%S")
for ws, rl, s, u in [
("ws-1", "", "", ""),
("", "high", "", ""),
("ws-1", "high", "", ""),
("ws-2", "low", "", ""),
("", "", one_minute_ago, one_minute_later),
("ws-1", "high", one_minute_ago, one_minute_later),
]:
count = db.count_output_assessments(ws_id=ws, risk_level=rl, since=s, until=u)
listed = db.list_output_assessments(ws_id=ws, risk_level=rl, since=s, until=u)
assert count == len(listed), (
f"Mismatch for ws_id={ws!r}, risk_level={rl!r}, since={s!r}, until={u!r}"
)
# ---------------------------------------------------------------------------
# Tier tie-breaker — when heuristic and llm rows share a second-resolution
# `created` value (the common case for two rows on the same call_id), the
# llm row must sort first so downstream consumers see the acted verdict.
# ---------------------------------------------------------------------------
class TestOutputAssessmentTierOrdering:
def test_llm_wins_tie_on_same_created(self, db):
# Two rows on the same call_id with the SAME `created` timestamp —
# without the tier tie-breaker the order is randomised by
# assessment_id (UUID). With the tie-breaker, llm sorts first.
# The insert path writes `created = now`, so back-to-back inserts
# within the same wall-clock second already tie naturally.
db.record_output_assessment(
**_make_assessment_kwargs(
assessment_id="oa_h",
call_id="tc_tied",
tier="heuristic",
)
)
db.record_output_assessment(
**_make_assessment_kwargs(
assessment_id="oa_l",
call_id="tc_tied",
tier="llm",
reasoning="judged",
judge_model="gpt-5-mini",
latency_ms=42,
)
)
rows = db.list_output_assessments()
# Two rows for the same call_id; llm must be first.
assert len(rows) == 2
assert rows[0]["tier"] == "llm"
assert rows[1]["tier"] == "heuristic"
def test_single_tier_ordering_unchanged(self, db):
# Single-tier rows (no LLM stage) should still sort by created DESC
# — the tie-breaker only kicks in when timestamps match exactly.
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa_old", call_id="tc_a")
)
db.record_output_assessment(
**_make_assessment_kwargs(assessment_id="oa_new", call_id="tc_b")
)
rows = db.list_output_assessments()
# Most recent first; with both at "heuristic" tier the secondary
# sort falls through to assessment_id DESC, but the key invariant
# is that listing produces both rows in a deterministic order.
assert len(rows) == 2
assert {r["assessment_id"] for r in rows} == {"oa_old", "oa_new"}