4 Commits

Author	SHA1	Message	Date
Patrick Buckley	110d44b07e	refactor(tools): remove man, math, and plan_agent built-in tools ... `man` and `math` duplicated capabilities already reachable through `bash`; `plan_agent` is better expressed as a `task_agent` running a planning skill, and carried a large amount of special-case machinery (plan-review gate, refinement loop, per-kind model routing). Removing all three shrinks the tool surface and cuts per-call token cost. Also removed, as dead-once-the-tools-are-gone: - the `math` sandbox executor (`turnstone.core.sandbox`) and its `[sandbox]` extra; the eval analyst now runs bash-only - the read-only `AGENT_TOOLS` sub-agent tool set and the `agent` tool-metadata key (`task_agent`/`TASK_AGENT_TOOLS` retained) - the plan-review protocol end to end: the `on_plan_review` UI hook, `resolve_plan`, `POST /v1/api/plan` + `POST /v1/api/route/plan`, the `plan_review`/`plan_resolved` SSE events, and their Python SDK / TypeScript SDK / OpenAPI / frontend / Discord+Slack bindings - the `model.plan_alias` / `model.plan_effort` settings and the registry `plan_model` / `plan_effort` routing fields TOOLS 31->28, TASK_AGENT_TOOLS 13->11; COORDINATOR_TOOLS unchanged. BREAKING CHANGE: removes the `man`, `math`, `plan_agent` tools, the plan-review SSE/HTTP/SDK surface, and the plan_* model-routing settings from the experimental 1.6 line.	2026-05-31 19:54:43 -07:00
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
Patrick Buckley	29b850919f	feat(sse): refresh-resume for mid-stream page reloads ... Refreshing a coordinator or interactive workstream pane while the LLM is mid-stream now restores the partial assistant text + reasoning immediately and flips the composer back to stop-mode, instead of showing nothing until the response completes. Per-turn inflight buffers (`_ws_inflight_content`, `_ws_inflight_reasoning`, `_ws_inflight_seq`) on `SessionUIBase` are kept separate from the existing multi-turn `_ws_turn_content` buffer that drives the dashboard's IDLE-piggyback payload. New `on_turn_start` (top of send-loop, defensive) and `on_turn_committed` (right after `messages.append(assistant_msg)`, primary) lifecycle hooks reset inflight at turn boundaries. The seq counter is monotonic across turns so a long-lived subscriber's `snap_seq` cutoff stays valid for the lifetime of the connection — resetting per-turn would silently drop turn N+1's first M tokens (M = whatever was streamed pre-snapshot in turn N). `snapshot_and_consume_state_payload` also drains inflight at idle/error so cancel and exception paths don't leak stale text. New `register_listener_with_in_progress_snapshot` atomically registers a listener and snapshots the inflight buffers; `make_events_handler` emits a `state_change` event (so the JS busy machine flips to stop-mode) followed by a one-shot `in_progress_snapshot` after the kind-specific replay, then strips the internal `_seq` field from yielded live events while filtering against `snap_seq`. A per-listener shallow `dict` copy in the live drain prevents the multi-tab race where one listener's `del event["_seq"]` would corrupt another listener's filter view. `_synthesize_cancelled_results` now emits synthetic `on_tool_result` events for each cancelled tool so live coord tabs can drop the newly-additive `coord-tool-batch--running` indicator cleanly. The indicator now coexists with `--auto`/`--approved` (applied on `tool_info` and `approval_resolved` approved; removed when every row in the batch has a result), making live tool execution visually parallel to the replay-time orphan rendering. Frontend handlers in `app.js` (interactive) and `coordinator.js` (coord) absorb EventSource auto-reconnect re-replays via a length-based prefix check on the in-progress buffer. New `InProgressSnapshotEvent` + `StateChangeEvent` dataclasses in the Python and TypeScript SDKs with type guards. `_MAX_TURN_CONTENT_CHARS` lifted 256 KiB → 512 KiB (single constant for both buffers — headroom for current commercial models). Regression tests cover race-free composition under concurrent writers, seq-filter dedup invariants, the cross-turn seq monotonic invariant, idle/error inflight drain, synthesized `on_tool_result` on cancel (including UI-hook failure isolation), and the multi-listener shared-dict invariant.	2026-05-08 18:23:38 -07:00
Patrick Buckley	e901e859c7	fix: materialize skill resources to disk for subprocess access (#271) ... * fix: materialize skill resources to disk for subprocess access Skill-bundled scripts stored in skill_resources were loaded into memory but never written to disk, causing FileNotFoundError when the model tried to execute them. Write resources to a per-workstream temp directory on skill load, expose via SKILL_RESOURCES_DIR env var and PATH, clean up on skill change or session close. * fix: pre-flight validation warns when skill references missing resources Scan rendered skill content for path references (scripts/foo.py, etc.) and compare against bundled skill_resources. Warn via on_info if any referenced paths are not bundled, so operators see the gap at skill activation rather than at runtime FileNotFoundError. * fix: address PR #271 review feedback - Fix trailing colon in PATH when $PATH is empty (cwd-on-PATH risk) - Move try/except inside per-resource loop so one bad write doesn't abort all resources - Explicit encoding="utf-8" for deterministic writes across locales * fix: address PR #271 review round 2 - Normalize available paths in _validate_skill_resources() to match referenced paths (both sides use os.path.normpath now) - Fix flaky traversal test: assert inside base dir, not escaped path	2026-03-31 22:34:24 -07:00