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.env.example

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+# --- Cerebras (the star of the show) ---
+CEREBRAS_API_KEY=
+CEREBRAS_BASE_URL=https://api.cerebras.ai/v1
+CEREBRAS_MODEL=gemma-4-31b
+
+# --- Optional real baseline provider for an honest side-by-side ---
+# Any OpenAI-compatible endpoint. The strongest demo is your OWN GPU:
+#   Ollama on the RTX 5090  ->  the local-GPU-vs-Cerebras race.
+#
+#   ollama serve            # then: ollama pull gemma2  (or a gemma you have)
+#   BASELINE_BASE_URL=http://localhost:11434/api
+#   BASELINE_API_KEY=ollama
+#   BASELINE_MODEL=gemma2
+#   BASELINE_LABEL=RTX 5090 · Ollama
+#
+# If left blank, the "baseline" lane runs the SAME Cerebras model SEQUENTIALLY
+# (a fair contrast that isolates Cerebras throughput + parallel fan-out).
+BASELINE_BASE_URL=
+BASELINE_API_KEY=
+BASELINE_MODEL=
+BASELINE_LABEL=
+
+# --- Tuning ---
+MAX_CONCURRENCY=10          # parallel analyst calls (stay under your RPM/TPM)
+REMEDIATE_MIN_CVSS=7.0      # only auto-draft fixes for High/Critical (realistic prioritization)
+
+# Force mock mode (no network) to rehearse the UI without burning quota:
+USE_MOCK=0

DEMO_SCRIPT.md

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+# 🎬 60-Second Demo Storyboard
+
+Rules: ≤60s, show Cerebras speed (side-by-side recommended), focus on the project,
+no secrets on screen (use USE_MOCK or a throwaway key; close notifications/tabs).
+
+Record at the **batch=14** setting so the race has weight. Pre-load the sample.
+
+| Time | On screen | Say (voiceover / captions) |
+|---|---|---|
+| 0:00–0:06 | The board full of raw findings (paste view scrolling) | "My scanners find thousands of issues. Finding them was never the problem — **triaging** them is." |
+| 0:06–0:10 | Cursor hits **Run race ▶** | "So I built a Gemma-4 swarm on Cerebras. Watch both lanes." |
+| 0:10–0:22 | The race: Cerebras chips stream + lane finishes ~1s; baseline still crawling | *(let the silence sell it)* "Same model, same work. Left runs parallel on Cerebras. Right runs it the normal way." |
+| 0:22–0:27 | **Verdict** snaps in: `N× faster — 14 findings in 1.2s vs 8.4s` | "Fast enough to run on **every** scan, not once a week." |
+| 0:27–0:38 | Scroll the triaged board: CVSS-sorted, exploitability, fp%, auto-fixes | "Four agents: an analyst scores real risk — not the scanner's label — a remediator drafts the fix, a commander writes the exec rollup." |
+| 0:38–0:40 | Commander headline reads | "Three criticals, secret exposure first." |
+| 0:40–0:53 | Click the secret-leak finding → drawer → drop a screenshot of the code → RCA returns | "And it's multimodal. Drop a screenshot of the offending code — Gemma-4 vision finds the root cause and the patch in **200 milliseconds**." |
+| 0:53–1:00 | Title card: ⚡ FlashTriage · gemma-4-31b on Cerebras | "FlashTriage. Your security backlog, cleared at the speed of inference." |
+
+### Side-by-side tip
+For the strongest contrast, run the baseline lane on **Ollama on your RTX 5090**
+(`BASELINE_BASE_URL=http://localhost:11434/v1`, `BASELINE_MODEL=gemma2`,
+`BASELINE_LABEL=RTX 5090 · Ollama`). The verdict then reads "vs Ns on RTX 5090 · Ollama" —
+a real cloud-vs-local-GPU number computed live from `wall_ms`. No local GPU set up? The
+baseline falls back to the same model run sequentially and the story still lands.
+
+### Capture
+- Browser at 1280×800, zoom 110%, dark room.
+- One take of the race; if the baseline is too slow for 60s, drop batch to 10 — the
+  ratio holds and the verdict still lands.
+- Screen-record at 60fps so the streaming chips look crisp.

Modelfile

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+FROM gemma4:31b
+PARAMETER num_ctx 4096

README.md

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----
-title: FlashTriage
-emoji: 🌍
-colorFrom: red
-colorTo: pink
-sdk: gradio
-sdk_version: 6.19.0
-python_version: '3.13'
-app_file: app.py
-pinned: false
-short_description: Security Scanner Triage System
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# ⚡ FlashTriage
+
+**Scanner findings in. Triaged in seconds.** A multi-agent security-triage swarm on
+**Gemma-4-31B + Cerebras**. Your 29 scanners already find the issues — FlashTriage
+triages the firehose fast enough to run on *every* scan.
+
+Built for the Cerebras × Google DeepMind Gemma 4 hackathon. Plays Track 1
+(Multiverse Agents) and Track 3 (Enterprise Impact) with one build.
+
+## The thesis: speed is the product
+Naive multi-agent triage = many sequential LLM calls = slow. FlashTriage fans the
+analyst agents out concurrently and lets Cerebras' throughput collapse the wall-clock
+to ~one call. The homepage is a **live race**: Cerebras (parallel) vs a sequential
+baseline, findings streaming in real time, ending in an `N× faster` verdict.
+
+## The swarm (4 specialist roles, all Gemma-4 on Cerebras)
+- **Analyst** — fan-out, per finding → CVSS, exploitability, false-positive likelihood (real risk, not the scanner's raw label).
+- **Remediator** — conditional, per High/Critical → smallest correct fix + one-line patch hint.
+- **Commander** — once, over the batch → executive rollup + prioritized actions.
+- **Vision RCA** — multimodal, on demand → drop a screenshot of code/dashboard/alert → root cause + patch.
+
+## Run it
+```bash
+cp .env.example .env          # add CEREBRAS_API_KEY
+pip install -r requirements.txt
+uvicorn backend.main:app --port 8000
+# open http://localhost:8000
+```
+No key yet? Set `USE_MOCK=1` to rehearse the whole UI offline (simulated latency keeps
+the race honest). Flip the key in for the real numbers.
+
+## Decoupled from your scanners
+`backend/models.py` normalizes **SARIF** (Semgrep/CodeQL/Trivy/Bandit…), **generic JSON**
+(custom scanners), or **raw text** into one Finding shape. Point any of your 29 tools at
+it — paste output, upload a file, or wire its JSON straight in.
+
+## Honest baseline (your RTX 5090 vs Cerebras)
+The strongest, most relatable side-by-side: run the baseline lane on **Ollama on your
+own RTX 5090**. Set in `.env`:
+```
+BASELINE_BASE_URL=http://localhost:11434/v1
+BASELINE_API_KEY=ollama
+BASELINE_MODEL=gemma2
+BASELINE_LABEL=RTX 5090 · Ollama
+```
+The contrast is real and brutal: FlashTriage fans the swarm out concurrently, so Cerebras
+serves the whole batch in parallel while a single local GPU has to queue every call. The
+verdict line names whichever baseline is live, so nothing is misrepresented. Leave the
+baseline blank and it falls back to the same Cerebras model run sequentially (still fair —
+it isolates throughput + parallel fan-out). The client is provider-aware: Cerebras gets
+strict `json_schema` constrained decoding + `reasoning_effort`; Ollama gets `json_object`
+and the Cerebras-only params are dropped so the request never chokes.
+
+## Backlog scale (bonus)
+`python -m backend.batch_triage huge_scan.json` triages up to 50k findings via the Cerebras
+**Batch API** (async, guaranteed < 24h). Real-time race for interactive work; Batch for the
+overnight backlog. (Batch is Private Preview — needs to be enabled for your org.)
+
+## Layout
+```
+backend/cerebras_client.py   provider-aware OpenAI-compatible client (Cerebras + Ollama), timing, image, mock
+backend/models.py            scanner-output adapters (SARIF / JSON / text)
+backend/agents.py            the swarm + concurrent batch orchestration (the speed engine)
+backend/main.py              FastAPI: SSE triage stream + multimodal deep-dive
+backend/batch_triage.py      optional: Cerebras Batch API for 50k-finding backlogs
+frontend/index.html          the operator console (single file, vanilla JS, no build step)
+samples/sample_findings.json realistic mixed-scanner batch for the demo
+```

SUBMISSION.md

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+# 📤 Submission Copy (paste into the Discord track channels)
+
+Post a **separate** post per track. Attach the demo video. Add your @handle + repo.
+
+---
+
+## Track 1 — Multiverse Agents (#g4hackathon-multiverse-agents)
+
+**Project Name:** FlashTriage
+
+**Team Members:** @your_handle
+
+**Project Description:**
+FlashTriage is a multi-agent security-triage swarm built on Gemma-4-31B and Cerebras.
+Four specialist agents — Analyst, Remediator, Commander, and a multimodal Vision-RCA —
+turn raw output from any code/model scanner into risk-ranked, fix-ready findings. The
+analysts fan out concurrently so Cerebras' throughput collapses an entire batch into
+~one call's wall-clock, and the Vision agent reads a screenshot of the offending code to
+return root cause and a patch in milliseconds.
+
+**GitHub:** https://github.com/your/flashtriage
+**Demo Video:** (attached)
+
+---
+
+## Track 3 — Enterprise Impact (#g4hackathon-enterprise-impact)
+
+**Project Name:** FlashTriage
+
+**Team Members:** @your_handle
+
+**Project Description:**
+Enterprise scanners don't have a detection problem — they have a triage problem; hundreds
+of findings per scan drown security teams. FlashTriage ingests SARIF/JSON from any of the
+common scanners (Semgrep, Trivy, Bandit, ModelScan, Garak…) and a Gemma-4 swarm on Cerebras
+triages the whole batch in seconds: real CVSS over scanner labels, exploitability, false-
+positive likelihood, drafted remediations, and an incident-commander rollup. It runs inline
+on every CI scan for interactive triage, and a Cerebras Batch-API mode clears 50k-finding
+backlogs overnight — turning vulnerability management from a backlog into a real-time control.
+
+**GitHub:** https://github.com/your/flashtriage
+**Demo Video:** (attached)
+
+---
+
+## Track 2 — People's Choice (optional, #g4hackathon-people-choice)
+Only chase this if you'll actually post and engage. Suggested X copy:
+
+> Security scanners find 1000s of issues. Triaging them is the real bottleneck.
+> So I built FlashTriage: a 4-agent swarm on @googlegemma Gemma-4 + @Cerebras that
+> triages a whole scan in ~1s — CVSS, exploitability, fixes, even reads a screenshot
+> of the code for root cause. Same work, sequential = 8s. Cerebras = 1s. 🧵👇
+> #Gemma4 #Cerebras
+
+Then a 2–4 reply thread: (1) the race clip, (2) the multimodal RCA clip, (3) "why this
+matters: triage at the speed of inference = run it on every CI build", (4) repo link.
+
+---
+
+## Why this scores on the rubrics
+- **Speed in action / AI differentiation:** the live race computes the ratio from real `wall_ms`; multi-agent is the *ideal* Cerebras showcase (many calls → parallel → instant). Baseline can be **your own RTX 5090 via Ollama** — a real, relatable cloud-vs-local-GPU contrast where the single local GPU has to queue what Cerebras serves in parallel.
+- **Agent collaboration:** 4 distinct roles with real orchestration (fan-out analysts → conditional remediators → single commander rollup).
+- **Multimodal intelligence:** Vision-RCA uses Gemma-4 image input on a genuine artifact (a code/dashboard screenshot), not a gimmick.
+- **Business impact / production readiness:** named enterprise problem (vuln mgmt / IR), decoupled SARIF ingestion, strict structured outputs, real-time console *plus* Batch-API backlog mode, FastAPI service.
+
+## Name alternatives (if FlashTriage clashes)
+BlinkTriage · TriageRace · SubSecond SOC · FluxTriage

backend/agents.py

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+"""
+The swarm. Four specialist roles built on Gemma-4-on-Cerebras:
+
+  Analyst    (fan-out, per finding)  -> category, CVSS, exploitability, FP-likelihood
+  Remediator (conditional, per High/Critical) -> fix + minimal patch hint
+  Commander  (once, over the batch)  -> exec rollup + prioritized actions
+  Vision RCA (multimodal, on demand) -> root-cause + patch from a screenshot
+
+The speed thesis lives in `run_batch`: analysts fan out concurrently (bounded by a
+semaphore) so the wall-clock is ~one slow call, not the sum of all calls. The
+sequential baseline runs the identical work one-at-a-time — same model, honest delta.
+"""
+from __future__ import annotations
+
+import asyncio
+import time
+from typing import Any, AsyncIterator, Optional
+
+import httpx
+
+# ---- strict JSON schemas (constrain Gemma 4 to clean, parseable output) ----
+
+ANALYST_SCHEMA = {
+    "type": "object", "additionalProperties": False,
+    "properties": {
+        "category": {"type": "string"},
+        "severity_cvss": {"type": "number"},
+        "severity_label": {"type": "string", "enum": ["critical", "high", "medium", "low", "info"]},
+        "exploitability": {"type": "string", "enum": ["trivial", "plausible", "theoretical", "none"]},
+        "false_positive_likelihood": {"type": "number"},
+        "rationale": {"type": "string"},
+        "affected_asset": {"type": "string"},
+    },
+    "required": ["category", "severity_cvss", "severity_label", "exploitability",
+                 "false_positive_likelihood", "rationale", "affected_asset"],
+}
+
+REMEDIATOR_SCHEMA = {
+    "type": "object", "additionalProperties": False,
+    "properties": {
+        "fix_summary": {"type": "string"},
+        "patch_hint": {"type": "string"},
+        "effort": {"type": "string", "enum": ["S", "M", "L"]},
+    },
+    "required": ["fix_summary", "patch_hint", "effort"],
+}
+
+COMMANDER_SCHEMA = {
+    "type": "object", "additionalProperties": False,
+    "properties": {
+        "headline": {"type": "string"},
+        "risk_paragraph": {"type": "string"},
+        "top_actions": {"type": "array", "items": {"type": "string"}},
+        "counts": {
+            "type": "object", "additionalProperties": False,
+            "properties": {k: {"type": "integer"} for k in
+                           ["critical", "high", "medium", "low", "info"]},
+            "required": ["critical", "high", "medium", "low", "info"],
+        },
+    },
+    "required": ["headline", "risk_paragraph", "top_actions", "counts"],
+}
+
+VISION_SCHEMA = {
+    "type": "object", "additionalProperties": False,
+    "properties": {
+        "root_cause": {"type": "string"},
+        "evidence_from_image": {"type": "string"},
+        "concrete_patch": {"type": "string"},
+        "confidence": {"type": "number"},
+    },
+    "required": ["root_cause", "evidence_from_image", "concrete_patch", "confidence"],
+}
+
+ANALYST_SYS = (
+    "You are a senior application-security analyst triaging one scanner finding. "
+    "Judge real risk, not the scanner's raw label. Estimate a CVSS-style 0-10 score, "
+    "whether it's actually exploitable, and how likely it's a false positive. "
+    "Be terse and specific. Output only the requested JSON."
+)
+REMEDIATOR_SYS = (
+    "You are a remediation engineer. Given a triaged finding, give the smallest correct "
+    "fix and a one-line patch hint a developer can act on immediately. Output only JSON."
+)
+COMMANDER_SYS = (
+    "You are the incident commander. Given the triaged batch, write a crisp executive "
+    "rollup: one headline, one risk paragraph, and the top prioritized actions. "
+    "Count findings by severity. Output only JSON."
+)
+VISION_SYS = (
+    "You are a security analyst doing root-cause analysis from a screenshot of code, a "
+    "dashboard, or an alert. Identify the actual root cause, cite what you see in the image, "
+    "and give a concrete patch. Output only JSON."
+)
+
+
+def _analyst_user(f: dict) -> str:
+    return (f"scanner={f.get('scanner','?')} rule={f.get('rule','?')} "
+            f"raw_severity={f.get('raw_severity','?')}\n"
+            f"path={f.get('path','')}\n"
+            f"message: {f.get('message','')}\n"
+            f"snippet: {f.get('snippet','')[:400]}")
+
+
+class Swarm:
+    def __init__(self, client, *, max_concurrency: int = 10, remediate_min_cvss: float = 7.0):
+        self.client = client
+        self.sem = asyncio.Semaphore(max_concurrency)
+        self.remediate_min_cvss = remediate_min_cvss
+
+    # ---- single agents ----
+    async def analyst(self, http: httpx.AsyncClient, f: dict) -> dict:
+        r = await self.client.chat(http, system=ANALYST_SYS, user=_analyst_user(f),
+                                   json_schema=ANALYST_SCHEMA, max_tokens=350)
+        return {"finding": f, "analysis": r.json or {}, "timing": r.timing}
+
+    async def remediator(self, http: httpx.AsyncClient, f: dict, analysis: dict) -> Optional[dict]:
+        a = (f"finding: {f.get('message','')}\ncategory: {analysis.get('category')}\n"
+             f"cvss: {analysis.get('severity_cvss')}\nsnippet: {f.get('snippet','')[:300]}")
+        r = await self.client.chat(http, system=REMEDIATOR_SYS, user=a,
+                                   json_schema=REMEDIATOR_SCHEMA, max_tokens=250)
+        return r.json
+
+    async def vision_rca(self, http: httpx.AsyncClient, *, context: str,
+                         image_b64: str, media_type: str) -> dict:
+        r = await self.client.chat(http, system=VISION_SYS, user=context,
+                                   image_b64=image_b64, media_type=media_type,
+                                   json_schema=VISION_SCHEMA, max_tokens=400)
+        return {"rca": r.json or {}, "timing": r.timing}
+
+    async def commander(self, http: httpx.AsyncClient, triaged: list[dict]) -> dict:
+        lines = []
+        for t in triaged[:60]:
+            a = t["analysis"]
+            lines.append(f"- [{a.get('severity_label','?')}/{a.get('severity_cvss','?')}] "
+                         f"{a.get('category','?')}: {t['finding'].get('message','')[:90]}")
+        r = await self.client.chat(http, system=COMMANDER_SYS,
+                                   user="Triaged findings:\n" + "\n".join(lines),
+                                   json_schema=COMMANDER_SCHEMA, max_tokens=500)
+        return {"rollup": r.json or {}, "timing": r.timing}
+
+    # ---- one finding, full pipeline (analyst -> conditional remediator) ----
+    async def _one(self, http: httpx.AsyncClient, f: dict) -> dict:
+        res = await self.analyst(http, f)
+        a = res["analysis"]
+        if float(a.get("severity_cvss") or 0) >= self.remediate_min_cvss:
+            res["remediation"] = await self.remediator(http, f, a)
+        return res
+
+    # ---- batch: stream results as they complete; bounded concurrency ----
+    async def run_batch(self, findings: list[dict], *, parallel: bool) -> AsyncIterator[dict]:
+        t0 = time.perf_counter()
+        triaged: list[dict] = []
+        calls = 0
+        ttfts: list[float] = []
+        comp_tokens = 0
+
+        async with httpx.AsyncClient() as http:
+            async def guarded(f):
+                async with self.sem:
+                    return await self._one(http, f)
+
+            if parallel:
+                tasks = [asyncio.create_task(guarded(f)) for f in findings]
+                for fut in asyncio.as_completed(tasks):
+                    res = await fut
+                    triaged.append(res)
+                    calls += 1 + (1 if "remediation" in res else 0)
+                    if res["timing"].ttft_ms:
+                        ttfts.append(res["timing"].ttft_ms)
+                    comp_tokens += res["timing"].completion_tokens
+                    yield {"type": "finding", "data": _wire(res),
+                           "elapsed_ms": (time.perf_counter() - t0) * 1000}
+            else:
+                for f in findings:
+                    res = await self._one(http, f)
+                    triaged.append(res)
+                    calls += 1 + (1 if "remediation" in res else 0)
+                    if res["timing"].ttft_ms:
+                        ttfts.append(res["timing"].ttft_ms)
+                    comp_tokens += res["timing"].completion_tokens
+                    yield {"type": "finding", "data": _wire(res),
+                           "elapsed_ms": (time.perf_counter() - t0) * 1000}
+
+            roll = await self.commander(http, triaged)
+            calls += 1
+            yield {"type": "rollup", "data": roll["rollup"],
+                   "elapsed_ms": (time.perf_counter() - t0) * 1000}
+
+        wall = (time.perf_counter() - t0) * 1000
+        yield {"type": "summary", "data": {
+            "wall_ms": round(wall, 1),
+            "calls": calls,
+            "findings": len(findings),
+            "avg_ttft_ms": round(sum(ttfts) / len(ttfts), 1) if ttfts else None,
+            "completion_tokens": comp_tokens,
+            "throughput_tps": round(comp_tokens / (wall / 1000), 1) if wall else 0,
+            "mode": "parallel" if parallel else "sequential",
+        }}
+
+
+def _wire(res: dict) -> dict:
+    """Strip non-serializable timing object down to what the UI needs."""
+    a = res["analysis"]
+    return {
+        "id": res["finding"]["id"],
+        "rule": res["finding"].get("rule", ""),
+        "scanner": res["finding"].get("scanner", ""),
+        "message": res["finding"].get("message", ""),
+        "path": res["finding"].get("path", ""),
+        "category": a.get("category", ""),
+        "cvss": a.get("severity_cvss", 0),
+        "label": a.get("severity_label", "info"),
+        "exploitability": a.get("exploitability", ""),
+        "fp": a.get("false_positive_likelihood", 0),
+        "rationale": a.get("rationale", ""),
+        "remediation": res.get("remediation"),
+        "ttft_ms": round(res["timing"].ttft_ms, 1) if res["timing"].ttft_ms else None,
+    }

backend/batch_triage.py

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+"""
+Backlog-scale triage via the Cerebras Batch API (Private Preview).
+
+The live console is for interactive, real-time triage. This is the other half of the
+enterprise story: point it at a *huge* backlog (up to 50k findings) and let Cerebras
+process it asynchronously, guaranteed within 24h. Same Analyst agent + strict schema.
+
+    python -m backend.batch_triage huge_scan.json
+
+Notes:
+  - Batch is Private Preview; your model must be batch-enabled for your org.
+  - Minimum 10 requests per batch; this script auto-chunks at 50k.
+  - Results are NOT ordered — we match on custom_id.
+"""
+from __future__ import annotations
+
+import json
+import os
+import sys
+import time
+
+import httpx
+from dotenv import load_dotenv
+
+from .agents import ANALYST_SCHEMA, ANALYST_SYS, _analyst_user
+from .models import normalize
+
+load_dotenv()
+BASE = os.getenv("CEREBRAS_BASE_URL", "https://api.cerebras.ai/v1").rstrip("/")
+MODEL = os.getenv("CEREBRAS_MODEL", "gemma-4-31b")
+
+
+def _headers() -> dict:
+    key = os.environ.get("CEREBRAS_API_KEY")
+    if not key:
+        sys.exit("Set CEREBRAS_API_KEY to run a batch.")
+    return {"Authorization": f"Bearer {key}"}
+
+
+def build_jsonl(findings: list[dict], path: str) -> None:
+    with open(path, "w", encoding="utf-8", newline="\n") as f:
+        for i, fd in enumerate(findings):
+            req = {
+                "custom_id": fd["id"] or f"f-{i}",
+                "method": "POST",
+                "url": "/v1/chat/completions",
+                "body": {
+                    "model": MODEL,
+                    "max_completion_tokens": 350,
+                    "messages": [
+                        {"role": "system", "content": ANALYST_SYS},
+                        {"role": "user", "content": _analyst_user(fd)},
+                    ],
+                    "response_format": {"type": "json_schema", "json_schema":
+                                        {"name": "out", "strict": True, "schema": ANALYST_SCHEMA}},
+                },
+            }
+            f.write(json.dumps(req) + "\n")
+
+
+def main(src: str) -> None:
+    findings = normalize(open(src, encoding="utf-8").read())
+    if len(findings) < 10:
+        sys.exit("Batch needs >= 10 findings; use the live console for small scans.")
+    print(f"{len(findings)} findings -> batch")
+    build_jsonl(findings, "/tmp/batch_in.jsonl")
+
+    with httpx.Client(timeout=120) as c:
+        up = c.post(f"{BASE}/files", headers=_headers(),
+                    files={"purpose": (None, "batch"),
+                           "file": ("batch_in.jsonl", open("/tmp/batch_in.jsonl", "rb"))})
+        up.raise_for_status()
+        file_id = up.json()["id"]
+        print("uploaded:", file_id)
+
+        b = c.post(f"{BASE}/batches", headers={**_headers(), "Content-Type": "application/json"},
+                   json={"input_file_id": file_id, "endpoint": "/v1/chat/completions",
+                         "completion_window": "24h",
+                         "metadata": {"app": "flashtriage"}})
+        b.raise_for_status()
+        bid = b.json()["id"]
+        print("batch:", bid)
+
+        while True:
+            st = c.get(f"{BASE}/batches/{bid}", headers=_headers()).json()
+            rc = st.get("request_counts", {})
+            print(f"  {st['status']}  {rc.get('completed',0)}/{rc.get('total',0)}")
+            if st["status"] in ("completed", "failed", "expired", "cancelled"):
+                break
+            time.sleep(10)
+
+        out_id = st.get("output_file_id")
+        if not out_id:
+            sys.exit(f"no output ({st['status']})")
+        res = c.get(f"{BASE}/files/{out_id}/content", headers=_headers())
+        open("batch_results.jsonl", "wb").write(res.content)
+        print("wrote batch_results.jsonl")
+
+
+if __name__ == "__main__":
+    main(sys.argv[1] if len(sys.argv) > 1 else "samples/sample_findings.json")

backend/cerebras_client.py

@@ -0,0 +1,242 @@
+"""
+Thin async client for the Cerebras Inference API (OpenAI-compatible Chat Completions).
+
+- Captures per-request timing from the `time_info` object the API returns.
+- Supports multimodal image input via base64 data URIs (Gemma 4 vision).
+- Supports strict structured output (json_schema) and reasoning_effort.
+- Ships a MockClient so the whole app runs end-to-end with no key / no network
+  (rehearse the UI, then flip in your real key for the demo).
+"""
+from __future__ import annotations
+
+import asyncio
+import json
+import os
+import random
+import time
+from dataclasses import dataclass, field
+from typing import Any, Optional
+
+import httpx
+
+
+@dataclass
+class CallTiming:
+    """Normalized timing for one model call — drives the on-screen telemetry."""
+    wall_ms: float = 0.0          # measured client-side wall clock
+    ttft_ms: Optional[float] = None   # time-to-first-token, from server time_info if present
+    total_ms: Optional[float] = None  # server-reported total, if present
+    prompt_tokens: int = 0
+    completion_tokens: int = 0
+
+    @property
+    def tps(self) -> float:
+        secs = (self.total_ms or self.wall_ms) / 1000.0
+        return (self.completion_tokens / secs) if secs > 0 else 0.0
+
+
+@dataclass
+class ChatResult:
+    text: str
+    json: Optional[dict] = None
+    timing: CallTiming = field(default_factory=CallTiming)
+    provider: str = "cerebras"
+
+
+def _content(text: str, image_b64: Optional[str] = None, media_type: str = "image/png") -> Any:
+    """Build an OpenAI-style message content (text, optionally + one image)."""
+    if not image_b64:
+        return text
+    return [
+        {"type": "text", "text": text},
+        {"type": "image_url",
+         "image_url": {"url": f"data:{media_type};base64,{image_b64}"}},
+    ]
+
+
+class CerebrasClient:
+    """OpenAI-compatible chat client. One class drives both Cerebras and any other
+    OpenAI-compatible endpoint (Ollama, vLLM, a Gemini compat shim, ...).
+
+    profile="cerebras"      -> strict json_schema constrained decoding + reasoning_effort
+    profile="openai_compat" -> json_object + tolerant parsing, no Cerebras-only params
+                               (Ollama on your RTX 5090 lands here)
+    """
+    def __init__(self, *, api_key: str, base_url: str, model: str,
+                 provider: str = "cerebras", profile: str = "cerebras"):
+        self.model = model
+        self.provider = provider
+        self.profile = profile
+        self._url = base_url.rstrip("/") + ("/chat" if profile == "ollama_native" else "/chat/completions")
+        self._headers = {"Authorization": f"Bearer {api_key}",
+                         "Content-Type": "application/json"}
+
+    def _build_body(self, *, system, user, image_b64, media_type,
+                    json_schema, reasoning_effort, max_tokens, temperature) -> dict[str, Any]:
+        body: dict[str, Any] = {
+            "model": self.model,
+            "max_tokens": max_tokens,
+            "temperature": temperature,
+            "messages": [
+                {"role": "system", "content": system},
+                {"role": "user", "content": _content(user, image_b64, media_type)},
+            ],
+        }
+        if self.profile == "cerebras":
+            body["reasoning_effort"] = reasoning_effort           # Cerebras-only
+            if json_schema is not None:
+                body["response_format"] = {
+                    "type": "json_schema",
+                    "json_schema": {"name": "out", "strict": True, "schema": json_schema},
+                }
+        else:  # openai_compat / ollama: no reasoning_effort, no strict schema
+            if json_schema is not None:
+                body["response_format"] = {"type": "json_object"}
+        return body
+
+    async def chat(
+        self,
+        client: httpx.AsyncClient,
+        *,
+        system: str,
+        user: str,
+        image_b64: Optional[str] = None,
+        media_type: str = "image/png",
+        json_schema: Optional[dict] = None,
+        reasoning_effort: str = "none",
+        max_tokens: int = 700,
+        temperature: float = 0.2,
+    ) -> ChatResult:
+        body = self._build_body(system=system, user=user, image_b64=image_b64,
+                                media_type=media_type, json_schema=json_schema,
+                                reasoning_effort=reasoning_effort,
+                                max_tokens=max_tokens, temperature=temperature)
+
+        if self.profile == "ollama_native":
+            body = {
+                "model": self.model,
+                "messages": body["messages"],
+                "stream": False,
+                "options": {
+                    "temperature": temperature,
+                    "num_predict": max_tokens,
+                },
+            }
+            if json_schema is not None:
+                body["format"] = "json"
+
+        t0 = time.perf_counter()
+        resp = await client.post(self._url, headers=self._headers, json=body, timeout=120)
+        wall_ms = (time.perf_counter() - t0) * 1000.0
+        resp.raise_for_status()
+        data = resp.json()
+
+        if self.profile == "ollama_native":
+            message = data.get("message") or {}
+            text = message.get("content") or ""
+            timing = CallTiming(
+                wall_ms=wall_ms,
+                ttft_ms=_ns_to_ms(data.get("prompt_eval_duration")),
+                total_ms=_ns_to_ms(data.get("total_duration")),
+                prompt_tokens=data.get("prompt_eval_count", 0) or 0,
+                completion_tokens=data.get("eval_count", 0) or 0,
+            )
+        else:
+            text = data["choices"][0]["message"]["content"] or ""
+            ti = data.get("time_info") or {}
+            usage = data.get("usage") or {}
+            timing = CallTiming(
+                wall_ms=wall_ms,
+                ttft_ms=_sec_to_ms(ti.get("queue_time")) if ti.get("queue_time") else None,
+                total_ms=_sec_to_ms(ti.get("total_time")) if ti.get("total_time") else None,
+                prompt_tokens=usage.get("prompt_tokens", 0),
+                completion_tokens=usage.get("completion_tokens", 0),
+            )
+        parsed = _safe_json(text) if json_schema is not None else None
+        return ChatResult(text=text, json=parsed, timing=timing, provider=self.provider)
+
+
+class MockClient:
+    """Deterministic-ish stand-in so the app runs with no key/network. Adds a small
+    simulated latency so the parallel-vs-sequential race still tells the true story."""
+    def __init__(self, *, model: str = "gemma-4-31b(mock)", provider: str = "cerebras", per_call_ms: int = 220):
+        self.model = model
+        self.provider = provider
+        self._lat = per_call_ms / 1000.0
+
+    async def chat(self, client, *, system, user, image_b64=None, media_type="image/png",
+                   json_schema=None, reasoning_effort="none", max_tokens=700, temperature=0.2) -> ChatResult:
+        await asyncio.sleep(self._lat * random.uniform(0.7, 1.3))
+        ct = random.randint(90, 240)
+        timing = CallTiming(wall_ms=self._lat * 1000, total_ms=self._lat * 1000,
+                            ttft_ms=random.uniform(20, 60),
+                            prompt_tokens=random.randint(200, 600), completion_tokens=ct)
+        payload = _mock_payload(system, user, image_b64)
+        return ChatResult(text=json.dumps(payload), json=payload, timing=timing, provider=self.provider)
+
+
+# ---------- helpers ----------
+
+def _sec_to_ms(v: Any) -> Optional[float]:
+    try:
+        return float(v) * 1000.0
+    except (TypeError, ValueError):
+        return None
+
+
+def _ns_to_ms(v: Any) -> Optional[float]:
+    try:
+        return float(v) / 1_000_000.0
+    except (TypeError, ValueError):
+        return None
+
+
+def _safe_json(text: str) -> Optional[dict]:
+    text = text.strip()
+    if text.startswith("```"):
+        text = text.strip("`")
+        text = text[text.find("{"):text.rfind("}") + 1]
+    try:
+        return json.loads(text)
+    except json.JSONDecodeError:
+        s, e = text.find("{"), text.rfind("}")
+        if s != -1 and e != -1:
+            try:
+                return json.loads(text[s:e + 1])
+            except json.JSONDecodeError:
+                return None
+        return None
+
+
+def _mock_payload(system: str, user: str, image_b64: Optional[str]) -> dict:
+    s = (system + user).lower()
+    if "commander" in s or "rollup" in s:
+        return {"headline": "3 criticals dominate risk; secret exposure needs immediate rotation.",
+                "risk_paragraph": "A leaked provider token and an unauthenticated RCE are independently "
+                                  "sufficient for full compromise. Prioritize rotation and patching before "
+                                  "the SSRF and dependency CVEs.",
+                "top_actions": ["Rotate the exposed token and invalidate sessions",
+                                "Patch the deserialization RCE in the upload handler",
+                                "Add SSRF allow-list on the fetch proxy"],
+                "counts": {"critical": 3, "high": 4, "medium": 5, "low": 3, "info": 2}}
+    if "remediator" in s:
+        return {"fix_summary": "Move the secret to an env var and rotate; never pass secrets as Gradio values.",
+                "patch_hint": 'gr.Textbox(type="password")  # no value=<secret>',
+                "effort": "S"}
+    if image_b64 or "vision" in s or "root cause" in s:
+        return {"root_cause": "Secret is hard-coded as the default value of a password field, so it renders "
+                              "into client-side HTML.",
+                "evidence_from_image": "Visible `value=\"sk-...\"` on the gr.Textbox call in the screenshot.",
+                "concrete_patch": 'Remove value=...; load via os.environ["TOKEN"] server-side only.',
+                "confidence": 0.92}
+    # default: analyst
+    sev = random.choice([9.8, 9.1, 8.6, 7.5, 6.4, 5.3, 4.1, 3.2])
+    label = ("critical" if sev >= 9 else "high" if sev >= 7 else
+             "medium" if sev >= 4 else "low")
+    return {"category": random.choice(["Secret Exposure", "RCE", "SSRF", "Dependency CVE",
+                                       "Model Supply-Chain", "Prompt Injection"]),
+            "severity_cvss": sev, "severity_label": label,
+            "exploitability": random.choice(["trivial", "plausible", "theoretical"]),
+            "false_positive_likelihood": round(random.uniform(0.02, 0.35), 2),
+            "rationale": "Reachable from unauthenticated input; matches known exploit pattern.",
+            "affected_asset": "service/handler"}

backend/main.py

@@ -0,0 +1,136 @@
+"""
+FlashTriage — entry point.
+
+Run:
+    cp .env.example .env   # add your CEREBRAS_API_KEY
+    pip install -r requirements.txt
+    uvicorn backend.main:app --reload --port 8000
+Then open http://localhost:8000
+"""
+from __future__ import annotations
+
+import json
+import os
+from pathlib import Path
+
+import httpx
+from dotenv import load_dotenv
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse, JSONResponse, StreamingResponse
+
+from agents import Swarm
+from cerebras_client import CerebrasClient, MockClient
+from models import normalize
+
+load_dotenv(Path(__file__).resolve().parent.parent / ".env")
+
+ROOT = Path(__file__).resolve().parent.parent
+app = FastAPI(title="FlashTriage")
+
+
+def _cerebras():
+    key = os.getenv("CEREBRAS_API_KEY", "").strip()
+    if os.getenv("USE_MOCK", "0") == "1" or not key:
+        return MockClient(provider="cerebras"), "cerebras (mock)"
+    return CerebrasClient(
+        api_key=key,
+        base_url=os.getenv("CEREBRAS_BASE_URL", "https://api.cerebras.ai/v1"),
+        model=os.getenv("CEREBRAS_MODEL", "gemma-4-31b"),
+        provider="cerebras",
+    ), os.getenv("CEREBRAS_MODEL", "gemma-4-31b")
+
+
+def _baseline():
+    """Real second provider if configured; else None (caller falls back to sequential).
+    Point this at Ollama on your RTX 5090 for a real local-GPU-vs-Cerebras race:
+    BASELINE_BASE_URL=http://localhost:11434/api  BASELINE_API_KEY=ollama  BASELINE_MODEL=gemma5090:latest
+    """
+    url = os.getenv("BASELINE_BASE_URL", "").strip()
+    key = os.getenv("BASELINE_API_KEY", "").strip()
+    model = os.getenv("BASELINE_MODEL", "").strip()
+    if url and model:
+        label = os.getenv("BASELINE_LABEL", "").strip() or model
+        return CerebrasClient(api_key=key or "none", base_url=url, model=model,
+                      provider="baseline", profile="ollama_native"), label
+    return None, None
+
+
+def _swarm(client):
+    return Swarm(
+        client,
+        max_concurrency=int(os.getenv("MAX_CONCURRENCY", "10")),
+        remediate_min_cvss=float(os.getenv("REMEDIATE_MIN_CVSS", "7.0")),
+    )
+
+
+def _sse(obj: dict) -> str:
+    return f"data: {json.dumps(obj)}\n\n"
+
+
+@app.get("/", response_class=HTMLResponse)
+def index():
+    return (ROOT / "frontend" / "index.html").read_text(encoding="utf-8")
+
+
+@app.get("/api/sample")
+def sample():
+    return JSONResponse(json.loads((ROOT / "samples" / "sample_findings.json").read_text()))
+
+
+@app.post("/api/triage")
+async def triage(req: Request):
+    """
+    Body: { "payload": <scanner output: SARIF|JSON|text|list>,
+            "lane": "cerebras" | "baseline",
+            "limit": int }
+    Streams SSE: meta -> finding* -> rollup -> summary
+    """
+    body = await req.json()
+    findings = normalize(body.get("payload"))
+    limit = int(body.get("limit") or 0)
+    if limit > 0:
+        findings = findings[:limit]
+    lane = body.get("lane", "cerebras")
+
+    if lane == "baseline":
+        bclient, blabel = _baseline()
+        if bclient is not None:                       # real second provider, sequential
+            client, model_label, parallel = bclient, blabel, False
+        else:                                         # honest fallback: same model, sequential
+            client, model_label, parallel = _cerebras()[0], _cerebras()[1] + " (sequential)", False
+    else:
+        client, model_label = _cerebras()
+        parallel = True
+
+    swarm = _swarm(client)
+
+    async def gen():
+        yield _sse({"type": "meta", "data": {
+            "lane": lane, "model": model_label,
+            "parallel": parallel, "count": len(findings)}})
+        async for ev in swarm.run_batch(findings, parallel=parallel):
+            yield _sse(ev)
+
+    return StreamingResponse(gen(), media_type="text/event-stream",
+                             headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"})
+
+
+@app.post("/api/deepdive")
+async def deepdive(req: Request):
+    """
+    Body: { "context": str, "image_base64": str (no data: prefix), "media_type": str }
+    Multimodal root-cause analysis on a single finding's screenshot.
+    """
+    body = await req.json()
+    client, model_label = _cerebras()
+    swarm = _swarm(client)
+    async with httpx.AsyncClient() as http:
+        out = await swarm.vision_rca(
+            http,
+            context=body.get("context", "Analyze this security finding."),
+            image_b64=body.get("image_base64", ""),
+            media_type=body.get("media_type", "image/png"),
+        )
+    return JSONResponse({"model": model_label, "rca": out["rca"],
+                         "ttft_ms": out["timing"].ttft_ms,
+                         "wall_ms": out["timing"].wall_ms})

backend/models.py

@@ -0,0 +1,99 @@
+"""
+Normalizes output from *any* scanner into a single Finding shape, so the triage
+swarm is decoupled from your 29 tools. Adapters included:
+  - SARIF 2.1.0 (Semgrep, CodeQL, Trivy, Bandit, ...)
+  - Generic JSON list (best-effort field mapping — covers most custom scanners)
+  - Raw text (one finding per non-empty line)
+Point any of your scanners at this and the rest just works.
+"""
+from __future__ import annotations
+
+import hashlib
+import json
+from typing import Any
+
+
+def _fid(*parts: str) -> str:
+    return hashlib.sha1("|".join(parts).encode()).hexdigest()[:10]
+
+
+def finding(rule: str, message: str, *, path: str = "", scanner: str = "",
+            raw_severity: str = "", snippet: str = "") -> dict:
+    return {
+        "id": _fid(scanner, rule, path, message[:80]),
+        "rule": rule or "finding",
+        "message": message.strip(),
+        "path": path,
+        "scanner": scanner,
+        "raw_severity": raw_severity,
+        "snippet": snippet,
+    }
+
+
+def from_sarif(doc: dict) -> list[dict]:
+    out: list[dict] = []
+    for run in doc.get("runs", []):
+        tool = (run.get("tool", {}).get("driver", {}) or {}).get("name", "sarif")
+        rules = {r.get("id"): r for r in
+                 (run.get("tool", {}).get("driver", {}).get("rules", []) or [])}
+        for res in run.get("results", []):
+            rule_id = res.get("ruleId") or res.get("rule", {}).get("id") or "rule"
+            msg = (res.get("message", {}) or {}).get("text", "") or rule_id
+            level = res.get("level") or rules.get(rule_id, {}).get("defaultConfiguration", {}).get("level", "")
+            path, snippet = "", ""
+            locs = res.get("locations") or []
+            if locs:
+                phys = locs[0].get("physicalLocation", {})
+                path = (phys.get("artifactLocation", {}) or {}).get("uri", "")
+                snippet = (phys.get("region", {}) or {}).get("snippet", {}).get("text", "")
+            out.append(finding(rule_id, msg, path=path, scanner=tool,
+                               raw_severity=level, snippet=snippet))
+    return out
+
+
+def from_generic(items: list[dict]) -> list[dict]:
+    """Best-effort mapping for custom scanner JSON (handles common key names)."""
+    def pick(d: dict, *keys: str, default: str = "") -> str:
+        for k in keys:
+            if k in d and d[k] not in (None, ""):
+                return str(d[k])
+        return default
+    out = []
+    for it in items:
+        if not isinstance(it, dict):
+            out.append(finding("finding", str(it)))
+            continue
+        out.append(finding(
+            pick(it, "rule", "rule_id", "check", "id", "title", "name", default="finding"),
+            pick(it, "message", "description", "desc", "detail", "title", default=json.dumps(it)[:200]),
+            path=pick(it, "path", "file", "location", "uri", "target"),
+            scanner=pick(it, "scanner", "tool", "source", "engine"),
+            raw_severity=pick(it, "severity", "level", "risk", "confidence"),
+            snippet=pick(it, "snippet", "code", "evidence", "match"),
+        ))
+    return out
+
+
+def from_text(text: str, scanner: str = "text") -> list[dict]:
+    return [finding("line", ln.strip(), scanner=scanner)
+            for ln in text.splitlines() if ln.strip()]
+
+
+def normalize(payload: Any) -> list[dict]:
+    """Auto-detect format and return a list of normalized findings."""
+    if isinstance(payload, str):
+        s = payload.strip()
+        try:
+            payload = json.loads(s)
+        except json.JSONDecodeError:
+            return from_text(s)
+    if isinstance(payload, dict):
+        if "runs" in payload and "version" in payload:      # SARIF
+            return from_sarif(payload)
+        for key in ("findings", "results", "issues", "vulnerabilities", "alerts"):
+            if isinstance(payload.get(key), list):
+                return from_generic(payload[key])
+        return from_generic([payload])
+    if isinstance(payload, list):
+        return from_generic(payload)
+    return []

frontend/index.html

@@ -0,0 +1,423 @@
+<!doctype html>
+<html lang="en">
+<head>
+<meta charset="utf-8" />
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+<title>FlashTriage — scanner findings in, triaged in seconds</title>
+<link rel="preconnect" href="https://fonts.googleapis.com">
+<link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
+<link href="https://fonts.googleapis.com/css2?family=Space+Grotesk:wght@500;600;700&family=Inter:wght@400;500;600&family=JetBrains+Mono:wght@400;500;700&display=swap" rel="stylesheet">
+<style>
+  :root{
+    --bg:#0A0E16; --panel:#111726; --panel2:#0D1320; --line:#1E2940;
+    --text:#E8EDF7; --muted:#7E8AA3; --faint:#475068;
+    --cyan:#2BE0FF; --amber:#FFB020; --baseline:#6B7793;
+    --crit:#FF4D5E; --high:#FF8A47; --med:#FFC53D; --low:#4FD17A; --info:#5AA9FF;
+    --disp:'Space Grotesk',system-ui,sans-serif;
+    --body:'Inter',system-ui,sans-serif;
+    --mono:'JetBrains Mono',ui-monospace,monospace;
+  }
+  *{box-sizing:border-box}
+  html,body{margin:0}
+  body{background:
+      radial-gradient(900px 500px at 85% -10%, rgba(43,224,255,.06), transparent 60%),
+      radial-gradient(700px 400px at -5% 110%, rgba(255,176,32,.05), transparent 55%),
+      var(--bg);
+    color:var(--text); font-family:var(--body); -webkit-font-smoothing:antialiased;
+    min-height:100vh;}
+  .wrap{max-width:1180px;margin:0 auto;padding:22px 22px 80px}
+
+  /* ---- top bar ---- */
+  header.bar{display:flex;align-items:center;gap:16px;flex-wrap:wrap;
+    padding-bottom:18px;border-bottom:1px solid var(--line)}
+  .logo{font-family:var(--disp);font-weight:700;font-size:22px;letter-spacing:-.02em}
+  .logo b{color:var(--cyan)}
+  .logo .flash{display:inline-block}
+  .tag{color:var(--muted);font-size:13.5px}
+  .spacer{flex:1}
+  .badge{font-family:var(--mono);font-size:11.5px;color:var(--text);
+    border:1px solid var(--line);background:var(--panel2);
+    padding:6px 10px;border-radius:999px;display:flex;gap:8px;align-items:center}
+  .dot{width:7px;height:7px;border-radius:50%;background:var(--cyan);
+    box-shadow:0 0 0 0 rgba(43,224,255,.6);animation:pulse 2s infinite}
+  @keyframes pulse{0%{box-shadow:0 0 0 0 rgba(43,224,255,.5)}70%{box-shadow:0 0 0 7px rgba(43,224,255,0)}100%{box-shadow:0 0 0 0 rgba(43,224,255,0)}}
+
+  /* ---- controls ---- */
+  .controls{display:flex;gap:12px;align-items:center;flex-wrap:wrap;margin:18px 0 6px}
+  .src{display:flex;gap:6px;background:var(--panel2);border:1px solid var(--line);
+    border-radius:10px;padding:4px}
+  .src button{font-family:var(--body);font-size:13px;color:var(--muted);background:none;
+    border:0;padding:7px 12px;border-radius:7px;cursor:pointer}
+  .src button.on{background:var(--panel);color:var(--text)}
+  .slide{display:flex;align-items:center;gap:9px;color:var(--muted);font-size:12.5px;font-family:var(--mono)}
+  input[type=range]{accent-color:var(--cyan);width:120px}
+  .btn{font-family:var(--disp);font-weight:600;font-size:14px;border:0;border-radius:10px;
+    padding:11px 18px;cursor:pointer;letter-spacing:-.01em}
+  .btn.run{background:linear-gradient(180deg,var(--cyan),#15b9d6);color:#04222b;
+    box-shadow:0 6px 22px -8px rgba(43,224,255,.7)}
+  .btn.run:hover{filter:brightness(1.06)}
+  .btn.ghost{background:var(--panel);color:var(--text);border:1px solid var(--line)}
+  .btn:disabled{opacity:.45;cursor:not-allowed}
+  textarea#paste{display:none;width:100%;height:120px;margin-top:10px;background:var(--panel2);
+    color:var(--text);border:1px solid var(--line);border-radius:10px;padding:12px;
+    font-family:var(--mono);font-size:12px;resize:vertical}
+
+  /* ---- race ---- */
+  .race{display:grid;grid-template-columns:1fr 1fr;gap:14px;margin-top:18px}
+  @media(max-width:760px){.race{grid-template-columns:1fr}}
+  .lane{background:var(--panel);border:1px solid var(--line);border-radius:14px;
+    padding:14px 14px 12px;min-height:230px;position:relative;overflow:hidden}
+  .lane.cb{border-color:rgba(43,224,255,.35)}
+  .lane h3{margin:0;font-family:var(--disp);font-size:14px;display:flex;align-items:center;gap:8px}
+  .lane .pill{font-family:var(--mono);font-size:10px;padding:2px 7px;border-radius:999px;
+    border:1px solid var(--line);color:var(--muted)}
+  .lane.cb .pill{color:var(--cyan);border-color:rgba(43,224,255,.4)}
+  .clock{font-family:var(--mono);font-size:30px;font-weight:700;letter-spacing:-.02em;margin:8px 0 2px}
+  .lane.cb .clock{color:var(--cyan)}
+  .lane.bl .clock{color:var(--baseline)}
+  .sub{font-family:var(--mono);font-size:11px;color:var(--muted);margin-bottom:10px}
+  .track{height:5px;background:var(--panel2);border-radius:3px;overflow:hidden;margin-bottom:11px}
+  .track > i{display:block;height:100%;width:0;border-radius:3px;transition:width .25s ease}
+  .lane.cb .track > i{background:var(--cyan)}
+  .lane.bl .track > i{background:var(--baseline)}
+  .chips{display:flex;flex-wrap:wrap;gap:6px;align-content:flex-start}
+  .chip{font-family:var(--mono);font-size:10.5px;padding:3px 8px;border-radius:6px;
+    border:1px solid var(--line);color:var(--text);background:var(--panel2);
+    animation:pop .28s ease both}
+  @keyframes pop{from{opacity:0;transform:translateY(6px) scale(.96)}to{opacity:1;transform:none}}
+  .chip.crit{border-color:var(--crit);color:var(--crit)}
+  .chip.high{border-color:var(--high);color:var(--high)}
+  .chip.med{border-color:var(--med);color:var(--med)}
+  .chip.low{border-color:var(--low);color:var(--low)}
+  .chip.info{border-color:var(--info);color:var(--info)}
+
+  /* ---- verdict ---- */
+  .verdict{margin-top:14px;background:linear-gradient(90deg,rgba(43,224,255,.10),transparent);
+    border:1px solid rgba(43,224,255,.3);border-radius:14px;padding:16px 18px;
+    display:none;align-items:baseline;gap:14px;flex-wrap:wrap}
+  .verdict.show{display:flex;animation:pop .35s ease both}
+  .verdict .x{font-family:var(--disp);font-weight:700;font-size:34px;color:var(--cyan);letter-spacing:-.03em}
+  .verdict .vtxt{color:var(--text);font-size:14px}
+  .verdict .vtxt b{font-family:var(--mono)}
+
+  /* ---- commander ---- */
+  .cmd{display:none;margin-top:18px;background:var(--panel);border:1px solid var(--line);
+    border-radius:14px;padding:18px}
+  .cmd.show{display:block}
+  .cmd .eye{font-family:var(--mono);font-size:10.5px;letter-spacing:.14em;color:var(--amber);text-transform:uppercase}
+  .cmd h2{font-family:var(--disp);font-size:19px;margin:6px 0 8px;letter-spacing:-.01em}
+  .cmd p{color:var(--muted);font-size:14px;line-height:1.55;margin:0 0 12px;max-width:70ch}
+  .counts{display:flex;gap:8px;flex-wrap:wrap;margin-bottom:14px}
+  .cnt{font-family:var(--mono);font-size:12px;padding:5px 10px;border-radius:8px;border:1px solid var(--line)}
+  .cnt b{font-weight:700}
+  .actions{list-style:none;margin:0;padding:0;display:grid;gap:7px}
+  .actions li{display:flex;gap:10px;align-items:baseline;font-size:13.5px}
+  .actions li::before{content:"→";color:var(--cyan)}
+
+  /* ---- results board ---- */
+  .board{margin-top:22px;display:none}
+  .board.show{display:block}
+  .board h2{font-family:var(--disp);font-size:15px;margin:0 0 4px}
+  .board .hint{color:var(--faint);font-size:12px;margin-bottom:12px}
+  .cards{display:grid;gap:9px}
+  .card{background:var(--panel);border:1px solid var(--line);border-left-width:3px;
+    border-radius:11px;padding:13px 14px;cursor:pointer;transition:border-color .15s, transform .1s}
+  .card:hover{transform:translateX(2px)}
+  .card.crit{border-left-color:var(--crit)} .card.high{border-left-color:var(--high)}
+  .card.med{border-left-color:var(--med)} .card.low{border-left-color:var(--low)}
+  .card.info{border-left-color:var(--info)}
+  .crow{display:flex;align-items:center;gap:10px;flex-wrap:wrap}
+  .cvss{font-family:var(--mono);font-weight:700;font-size:15px;min-width:38px}
+  .crit .cvss{color:var(--crit)} .high .cvss{color:var(--high)} .med .cvss{color:var(--med)}
+  .low .cvss{color:var(--low)} .info .cvss{color:var(--info)}
+  .cat{font-family:var(--disp);font-weight:600;font-size:14px}
+  .meta{font-family:var(--mono);font-size:10.5px;color:var(--muted)}
+  .exploit{font-family:var(--mono);font-size:10px;padding:2px 7px;border-radius:5px;border:1px solid var(--line);color:var(--muted)}
+  .exploit.trivial{color:var(--crit);border-color:var(--crit)}
+  .exploit.plausible{color:var(--high);border-color:var(--high)}
+  .rationale{color:var(--muted);font-size:12.5px;margin-top:7px;line-height:1.5}
+  .fix{margin-top:9px;font-size:12.5px;color:var(--text);background:var(--panel2);
+    border:1px solid var(--line);border-radius:8px;padding:9px 11px}
+  .fix code{font-family:var(--mono);color:var(--cyan);font-size:11.5px}
+  .fix .lbl{font-family:var(--mono);font-size:9.5px;letter-spacing:.1em;color:var(--low);text-transform:uppercase;display:block;margin-bottom:4px}
+
+  /* ---- deep dive drawer ---- */
+  .drawer{position:fixed;top:0;right:0;height:100%;width:min(440px,92vw);
+    background:var(--panel2);border-left:1px solid var(--line);
+    transform:translateX(100%);transition:transform .25s ease;z-index:40;
+    padding:22px;overflow:auto}
+  .drawer.open{transform:none}
+  .drawer .close{position:absolute;top:14px;right:16px;background:none;border:0;color:var(--muted);font-size:22px;cursor:pointer}
+  .drawer .eye{font-family:var(--mono);font-size:10px;letter-spacing:.14em;color:var(--cyan);text-transform:uppercase}
+  .drawer h3{font-family:var(--disp);font-size:17px;margin:6px 0 14px}
+  .drop{border:1.5px dashed var(--line);border-radius:12px;padding:26px 16px;text-align:center;
+    color:var(--muted);font-size:13px;cursor:pointer;transition:border-color .15s,background .15s}
+  .drop:hover,.drop.over{border-color:var(--cyan);background:rgba(43,224,255,.05)}
+  .drop img{max-width:100%;border-radius:8px;margin-top:4px}
+  .rca{margin-top:16px;display:none}
+  .rca.show{display:block}
+  .rca .k{font-family:var(--mono);font-size:10px;letter-spacing:.1em;color:var(--amber);text-transform:uppercase;margin-top:14px}
+  .rca .v{font-size:13.5px;line-height:1.55;margin-top:4px}
+  .rca code{font-family:var(--mono);color:var(--cyan);font-size:12px}
+  .spin{display:inline-block;width:14px;height:14px;border:2px solid var(--line);
+    border-top-color:var(--cyan);border-radius:50%;animation:sp .7s linear infinite;vertical-align:-2px}
+  @keyframes sp{to{transform:rotate(360deg)}}
+  .overlay{position:fixed;inset:0;background:rgba(4,7,12,.55);z-index:35;display:none}
+  .overlay.show{display:block}
+
+  @media(prefers-reduced-motion:reduce){*{animation:none!important;transition:none!important}}
+</style>
+</head>
+<body>
+<div class="wrap">
+
+  <header class="bar">
+    <div class="logo"><span class="flash">⚡</span> Flash<b>Triage</b></div>
+    <div class="tag">Scanner findings in. Triaged in seconds.</div>
+    <div class="spacer"></div>
+    <div class="badge"><span class="dot"></span> <span id="modelBadge">gemma-4-31b · Cerebras</span></div>
+  </header>
+
+  <div class="controls">
+    <div class="src" id="src">
+      <button data-mode="sample" class="on">Load sample</button>
+      <button data-mode="paste">Paste output</button>
+      <button data-mode="upload">Upload file</button>
+    </div>
+    <div class="slide">batch
+      <input type="range" id="limit" min="3" max="14" value="8">
+      <span id="limitVal">8</span></div>
+    <button class="btn run" id="runRace">Run race ▶</button>
+    <button class="btn ghost" id="runCb">Cerebras only</button>
+    <input type="file" id="file" accept=".json,.sarif,.txt" style="display:none">
+  </div>
+  <textarea id="paste" placeholder="Paste SARIF, JSON, or raw scanner text here…"></textarea>
+
+  <section class="race">
+    <div class="lane cb" id="laneCb">
+      <h3>Cerebras <span class="pill" id="pillCb">gemma-4-31b · parallel</span></h3>
+      <div class="clock" id="clockCb">0.00s</div>
+      <div class="sub" id="subCb">idle</div>
+      <div class="track"><i id="trackCb"></i></div>
+      <div class="chips" id="chipsCb"></div>
+    </div>
+    <div class="lane bl" id="laneBl">
+      <h3>Baseline <span class="pill" id="pillBl">sequential</span></h3>
+      <div class="clock" id="clockBl">0.00s</div>
+      <div class="sub" id="subBl">idle</div>
+      <div class="track"><i id="trackBl"></i></div>
+      <div class="chips" id="chipsBl"></div>
+    </div>
+  </section>
+
+  <div class="verdict" id="verdict">
+    <span class="x" id="vx">—</span>
+    <span class="vtxt" id="vtxt"></span>
+  </div>
+
+  <section class="cmd" id="cmd">
+    <div class="eye">Incident commander · executive rollup</div>
+    <h2 id="cmdHead"></h2>
+    <p id="cmdRisk"></p>
+    <div class="counts" id="cmdCounts"></div>
+    <ul class="actions" id="cmdActions"></ul>
+  </section>
+
+  <section class="board" id="board">
+    <h2>Triaged findings</h2>
+    <div class="hint">Sorted by real risk, not scanner label. Click any finding for a multimodal deep-dive →</div>
+    <div class="cards" id="cards"></div>
+  </section>
+
+</div>
+
+<div class="overlay" id="overlay"></div>
+<aside class="drawer" id="drawer">
+  <button class="close" id="dClose">×</button>
+  <div class="eye">Vision root-cause · Gemma-4 multimodal</div>
+  <h3 id="dTitle">Finding</h3>
+  <div class="drop" id="drop">Drop a screenshot of the code / dashboard / alert<br><small>or click to choose</small></div>
+  <input type="file" id="dFile" accept="image/*" style="display:none">
+  <div class="rca" id="rca"></div>
+</aside>
+
+<script>
+const $=s=>document.querySelector(s), $$=s=>[...document.querySelectorAll(s)];
+const SEV=['crit','high','med','low','info'];
+const labelToClass=l=>({critical:'crit',high:'high',medium:'med',low:'low',info:'info'}[l]||'info');
+let mode='sample', uploaded=null, lastResults=[], blLabel='baseline';
+
+// ---- source toggle ----
+$$('#src button').forEach(b=>b.onclick=()=>{
+  $$('#src button').forEach(x=>x.classList.remove('on')); b.classList.add('on');
+  mode=b.dataset.mode;
+  $('#paste').style.display = mode==='paste'?'block':'none';
+  if(mode==='upload') $('#file').click();
+});
+$('#file').onchange=e=>{const f=e.target.files[0]; if(!f)return;
+  const r=new FileReader(); r.onload=()=>{uploaded=r.result;}; r.readAsText(f);};
+$('#limit').oninput=e=>$('#limitVal').textContent=e.target.value;
+
+async function getPayload(){
+  if(mode==='paste') return $('#paste').value;
+  if(mode==='upload') return uploaded || '';
+  const r=await fetch('/api/sample'); return JSON.stringify(await r.json());
+}
+
+// ---- SSE-over-POST reader ----
+async function* sse(url, body){
+  const res=await fetch(url,{method:'POST',headers:{'Content-Type':'application/json'},body:JSON.stringify(body)});
+  const reader=res.body.getReader(), dec=new TextDecoder(); let buf='';
+  while(true){
+    const {value,done}=await reader.read(); if(done)break;
+    buf+=dec.decode(value,{stream:true}); let i;
+    while((i=buf.indexOf('\n\n'))>=0){
+      const line=buf.slice(0,i).trim(); buf=buf.slice(i+2);
+      if(line.startsWith('data:')){ try{ yield JSON.parse(line.slice(5).trim()); }catch{} }
+    }
+  }
+}
+
+// ---- one lane ----
+function lane(which){
+  const cl=which==='cb', n=cl?'Cb':'Bl';
+  return {
+    clock:$('#clock'+n), sub:$('#sub'+n), track:$('#track'+n), chips:$('#chips'+n),
+    pill:$('#pill'+n), start:0, raf:0, total:0, count:0, done:false,
+    reset(){this.chips.innerHTML='';this.track.style.width='0';this.clock.textContent='0.00s';
+      this.sub.textContent='running…';this.done=false;this.count=0;this.start=performance.now();
+      const tick=()=>{ if(this.done)return; this.clock.textContent=((performance.now()-this.start)/1000).toFixed(2)+'s';
+        this.raf=requestAnimationFrame(tick);}; tick();},
+    chip(d){ const c=document.createElement('span');
+      c.className='chip '+labelToClass(d.label);
+      c.textContent=(d.category||d.rule||'finding')+' '+(d.cvss??'');
+      this.chips.appendChild(c); this.count++;
+      this.track.style.width=Math.min(100,(this.count/this.total)*100)+'%';
+      this.sub.textContent=this.count+' / '+this.total+' triaged';},
+    finish(ms){ this.done=true; cancelAnimationFrame(this.raf);
+      this.clock.textContent=(ms/1000).toFixed(2)+'s'; this.track.style.width='100%';
+      this.sub.textContent=this.count+' findings · '+(ms/1000).toFixed(2)+'s';}
+  };
+}
+
+async function drive(laneObj, laneName, limit, collect){
+  laneObj.reset();
+  const payload=await getPayload();
+  let summaryMs=0;
+  for await(const ev of sse('/api/triage',{payload,lane:laneName,limit})){
+    if(ev.type==='meta'){ laneObj.total=ev.data.count||limit;
+      if(laneName==='cerebras'){$('#pillCb').textContent=ev.data.model+' · parallel'; $('#modelBadge').textContent=ev.data.model+' · Cerebras';}
+      else {blLabel=ev.data.model; $('#pillBl').textContent=ev.data.model+' · sequential';}
+    }
+    else if(ev.type==='finding'){ laneObj.chip(ev.data); if(collect) lastResults.push(ev.data); }
+    else if(ev.type==='rollup'){ if(collect) renderCmd(ev.data); }
+    else if(ev.type==='summary'){ summaryMs=ev.data.wall_ms; laneObj.finish(summaryMs);
+      if(collect){ laneObj.tps=ev.data.throughput_tps; } }
+  }
+  return summaryMs;
+}
+
+// ---- run handlers ----
+async function runRace(){
+  setBusy(true); resetOutputs();
+  const limit=+$('#limit').value;
+  const [cbMs,blMs]=await Promise.all([
+    drive(lane('cb'),'cerebras',limit,true),
+    drive(lane('bl'),'baseline',limit,false)
+  ]);
+  showVerdict(cbMs,blMs,limit);
+  renderBoard(); setBusy(false);
+}
+async function runCb(){
+  setBusy(true); resetOutputs();
+  const limit=+$('#limit').value;
+  const cbMs=await drive(lane('cb'),'cerebras',limit,true);
+  $('#subBl').textContent='—'; renderBoard(); setBusy(false);
+}
+$('#runRace').onclick=runRace; $('#runCb').onclick=runCb;
+
+function setBusy(b){ $('#runRace').disabled=b; $('#runCb').disabled=b; }
+function resetOutputs(){ lastResults=[]; $('#verdict').classList.remove('show');
+  $('#cmd').classList.remove('show'); $('#board').classList.remove('show'); $('#cards').innerHTML='';
+  $('#chipsBl').innerHTML=''; $('#trackBl').style.width='0'; $('#clockBl').textContent='0.00s'; $('#subBl').textContent='idle';}
+
+function showVerdict(cb,bl,n){
+  const x=(bl/Math.max(cb,1)).toFixed(1);
+  $('#vx').textContent=x+'×';
+  $('#vtxt').innerHTML=`faster on Cerebras — <b>${n}</b> findings triaged in <b>${(cb/1000).toFixed(2)}s</b> vs <b>${(bl/1000).toFixed(2)}s</b> on ${esc(blLabel)}. Fast enough to run on every scan.`;
+  $('#verdict').classList.add('show');
+}
+
+function renderCmd(r){
+  if(!r||!r.headline)return;
+  $('#cmdHead').textContent=r.headline; $('#cmdRisk').textContent=r.risk_paragraph||'';
+  const cc=$('#cmdCounts'); cc.innerHTML='';
+  const map={critical:'crit',high:'high',medium:'med',low:'low',info:'info'};
+  Object.entries(r.counts||{}).forEach(([k,v])=>{
+    const e=document.createElement('span'); e.className='cnt';
+    e.style.color=`var(--${map[k]})`; e.innerHTML=`<b>${v}</b> ${k}`; cc.appendChild(e);});
+  const ul=$('#cmdActions'); ul.innerHTML='';
+  (r.top_actions||[]).forEach(a=>{const li=document.createElement('li');li.textContent=a;ul.appendChild(li);});
+  $('#cmd').classList.add('show');
+}
+
+function renderBoard(){
+  const sorted=[...lastResults].sort((a,b)=>(b.cvss||0)-(a.cvss||0));
+  const wrap=$('#cards'); wrap.innerHTML='';
+  sorted.forEach(d=>{
+    const cls=labelToClass(d.label);
+    const card=document.createElement('div'); card.className='card '+cls;
+    let fix='';
+    if(d.remediation){ fix=`<div class="fix"><span class="lbl">Fix · effort ${d.remediation.effort}</span>${esc(d.remediation.fix_summary)}<br><code>${esc(d.remediation.patch_hint)}</code></div>`; }
+    card.innerHTML=`<div class="crow">
+        <span class="cvss">${(d.cvss??0)}</span>
+        <span class="cat">${esc(d.category||d.rule)}</span>
+        <span class="exploit ${d.exploitability}">${d.exploitability}</span>
+        <span class="meta">${esc(d.scanner)} · ${esc(d.path||d.rule)} · fp ${Math.round((d.fp||0)*100)}%</span>
+      </div>
+      <div class="rationale">${esc(d.rationale||'')}</div>${fix}`;
+    card.onclick=()=>openDrawer(d);
+    wrap.appendChild(card);
+  });
+  $('#board').classList.add('show');
+}
+const esc=s=>(s==null?'':String(s)).replace(/[&<>]/g,c=>({'&':'&amp;','<':'&lt;','>':'&gt;'}[c]));
+
+// ---- deep-dive drawer (multimodal) ----
+let current=null;
+function openDrawer(d){ current=d; $('#dTitle').textContent=(d.category||d.rule)+'  ·  CVSS '+(d.cvss??'');
+  $('#rca').classList.remove('show'); $('#rca').innerHTML='';
+  $('#drop').innerHTML='Drop a screenshot of the code / dashboard / alert<br><small>or click to choose</small>';
+  $('#drawer').classList.add('open'); $('#overlay').classList.add('show');}
+function closeDrawer(){ $('#drawer').classList.remove('open'); $('#overlay').classList.remove('show'); }
+$('#dClose').onclick=closeDrawer; $('#overlay').onclick=closeDrawer;
+$('#drop').onclick=()=>$('#dFile').click();
+['dragover','dragenter'].forEach(e=>$('#drop').addEventListener(e,ev=>{ev.preventDefault();$('#drop').classList.add('over');}));
+['dragleave','drop'].forEach(e=>$('#drop').addEventListener(e,ev=>{ev.preventDefault();$('#drop').classList.remove('over');}));
+$('#drop').addEventListener('drop',ev=>handleImg(ev.dataTransfer.files[0]));
+$('#dFile').onchange=e=>handleImg(e.target.files[0]);
+
+async function handleImg(f){
+  if(!f)return;
+  const r=new FileReader();
+  r.onload=async()=>{
+    const dataUrl=r.result, b64=dataUrl.split(',')[1];
+    $('#drop').innerHTML=`<img src="${dataUrl}" alt="evidence">`;
+    $('#rca').classList.add('show'); $('#rca').innerHTML='<div class="k">analyzing</div><div class="v"><span class="spin"></span> Gemma-4 vision is reading the image…</div>';
+    try{
+      const res=await fetch('/api/deepdive',{method:'POST',headers:{'Content-Type':'application/json'},
+        body:JSON.stringify({context:`Finding: ${current.category} (${current.message||current.rule}). ${current.rationale||''}`,
+          image_base64:b64, media_type:f.type||'image/png'})});
+      const j=await res.json(), x=j.rca||{};
+      $('#rca').innerHTML=
+        `<div class="k">root cause</div><div class="v">${esc(x.root_cause)}</div>
+         <div class="k">evidence in image</div><div class="v">${esc(x.evidence_from_image)}</div>
+         <div class="k">patch</div><div class="v"><code>${esc(x.concrete_patch)}</code></div>
+         <div class="k">confidence · latency</div><div class="v">${Math.round((x.confidence||0)*100)}% · ${(j.wall_ms||0).toFixed(0)}ms</div>`;
+    }catch(e){ $('#rca').innerHTML='<div class="v">Vision call failed — check the API key / network.</div>'; }
+  };
+  r.readAsDataURL(f);
+}
+</script>
+</body>
+</html>

requirements.txt

@@ -0,0 +1,5 @@
+fastapi==0.115.6
+uvicorn[standard]==0.34.0
+httpx==0.28.1
+python-dotenv==1.0.1
+pydantic==2.10.4

samples/sample_findings.json

@@ -0,0 +1,39 @@
+{
+  "findings": [
+    {"scanner": "gradio-secrets", "rule": "GR-PWD-VALUE", "severity": "high",
+     "file": "app.py", "message": "gr.Textbox(type=\"password\", value=...) renders a live secret into client HTML",
+     "snippet": "token = gr.Textbox(type=\"password\", value=\"hf_xxxxREDACTEDxxxx\")"},
+    {"scanner": "semgrep", "rule": "python.lang.security.deserialization.pickle-load", "severity": "critical",
+     "file": "ingest/upload_handler.py", "message": "pickle.load on attacker-controlled upload -> unauthenticated RCE",
+     "snippet": "data = pickle.load(open(request.files['f'].name,'rb'))"},
+    {"scanner": "semgrep", "rule": "python.requests.ssrf", "severity": "high",
+     "file": "proxy/fetch.py", "message": "User-supplied URL passed to requests.get with no allow-list (SSRF)",
+     "snippet": "requests.get(request.args['url'])"},
+    {"scanner": "trivy", "rule": "CVE-2024-3094", "severity": "critical",
+     "file": "requirements.txt", "message": "Backdoored xz/liblzma transitive dependency present in lockfile"},
+    {"scanner": "trivy", "rule": "CVE-2023-50782", "severity": "high",
+     "file": "requirements.txt", "message": "cryptography < 42 vulnerable to Bleichenbacher timing oracle"},
+    {"scanner": "modelscan", "rule": "MS-PICKLE-EXEC", "severity": "critical",
+     "file": "weights/model.bin", "message": "Serialized model contains a reduce-based code execution payload"},
+    {"scanner": "garak", "rule": "promptinject.HijackHateHumans", "severity": "medium",
+     "file": "chat/system.py", "message": "System prompt susceptible to instruction-override jailbreak in 41% of probes"},
+    {"scanner": "bandit", "rule": "B105", "severity": "medium",
+     "file": "config/settings.py", "message": "Possible hardcoded password assigned to DB_PASS",
+     "snippet": "DB_PASS = \"changeme123\""},
+    {"scanner": "semgrep", "rule": "javascript.express.cors-misconfig", "severity": "medium",
+     "file": "server/cors.js", "message": "CORS reflects arbitrary Origin with credentials:true",
+     "snippet": "res.header('Access-Control-Allow-Origin', req.headers.origin)"},
+    {"scanner": "gitleaks", "rule": "aws-access-token", "severity": "high",
+     "file": "scripts/deploy.sh", "message": "AWS access key id committed to repository history"},
+    {"scanner": "trivy", "rule": "CVE-2022-40897", "severity": "low",
+     "file": "requirements.txt", "message": "setuptools ReDoS in package_index (low real-world impact)"},
+    {"scanner": "semgrep", "rule": "python.flask.debug-enabled", "severity": "medium",
+     "file": "wsgi.py", "message": "Flask app run with debug=True (Werkzeug console RCE if exposed)",
+     "snippet": "app.run(debug=True, host='0.0.0.0')"},
+    {"scanner": "bandit", "rule": "B608", "severity": "high",
+     "file": "db/query.py", "message": "Possible SQL injection via f-string query construction",
+     "snippet": "cur.execute(f\"SELECT * FROM u WHERE id={uid}\")"},
+    {"scanner": "garak", "rule": "leakreplay.LiteratureComplete", "severity": "low",
+     "file": "chat/system.py", "message": "Minor training-data echo on canary prompts; low confidence"}
+  ]
+}