Metadata-Version: 2.4
Name: provael
Version: 0.3.0
Summary: Provael — a model-agnostic tool to red-team open Vision-Language-Action (VLA) robot policies in simulation and report an Attack Success Rate (ASR).
Project-URL: Homepage, https://github.com/provael/provael
Project-URL: Source, https://github.com/provael/provael
Project-URL: Issues, https://github.com/provael/provael/issues
Author: Sattyam Jain
Maintainer: Sattyam Jain
License-Expression: Apache-2.0
License-File: LICENSE
Keywords: adversarial,ai-safety,lerobot,red-team,robotics,security,vla
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Security
Requires-Python: >=3.12
Requires-Dist: numpy
Requires-Dist: pillow>=10
Requires-Dist: pydantic>=2.7
Requires-Dist: pyyaml
Requires-Dist: rich
Requires-Dist: typer>=0.12
Provides-Extra: lerobot
Requires-Dist: lerobot[smolvla]==0.5.1; extra == 'lerobot'
Description-Content-Type: text/markdown

<p align="center">
  <img src="https://raw.githubusercontent.com/provael/provael/main/docs/assets/provael_icon_512.png" alt="Provael logo" width="110" height="110">
</p>

# Provael

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[![PyPI](https://img.shields.io/pypi/v/provael.svg)](https://pypi.org/project/provael/)
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> **Prove it. Prevail.** — red-team open **Vision-Language-Action (VLA)** robot policies in
> simulation and report an **Attack Success Rate (ASR)**.

<p align="center">
  <img src="https://raw.githubusercontent.com/provael/provael/main/docs/assets/demo.svg" alt="Provael attack — ASR across instruction/visual/injection families" width="760">
</p>

<p align="center"><sub>Deterministic CPU run, seed 0 — regenerate with <code>./scripts/record_demo.sh</code>.</sub></p>

**Provael** is the open-source red-team & assurance layer for physical AI. This repo is its
core: a small, **model-agnostic** harness that perturbs the instructions and observations a
VLA policy receives inside a simulator and measures how often those perturbations drive the
policy into an *unsafe* state. The headline number is the ASR.

It ships **three families of templated, auditable attacks** — `instruction` (text
reframings), `visual` (observation-space markers), and `injection` (indirect / embodied
prompt injection) — plus a `none` baseline, an ASR **leaderboard**, and a gated adapter for
real **SmolVLA** policies on the **LIBERO** simulator. These are heuristic perturbations,
**not** gradient/optimization-based adversarial attacks — see
[Scope and honest limitations](#scope-and-honest-limitations).

The entire core — abstractions, attacks, scoring, runner, report, CLI, leaderboard — runs
and is tested on a **plain CPU with no GPU and no model/dataset download**, using a
deterministic `StubPolicy` + `StubSuite`. Real policies (SmolVLA via LeRobot) and the LIBERO
simulator live behind an optional extra and a `PROVAEL_INTEGRATION=1` gate.

> ⚠️ This is a **defensive, sim-only** tool for hardening policies via responsible
> disclosure. It drives no physical robots and ships no real-world-harm payloads.
> Read **[SAFETY.md](https://github.com/provael/provael/blob/main/SAFETY.md)** before using it.

## The Embodied AI Security Top 10

An independent, community risk list for the security of VLA models and the robots they drive — the
framework Provael's attacks map to. Read it: [docs/TOP10.md](docs/TOP10.md). Draft v0.2, PRs welcome.

Every attack is tagged with the risk it exercises; the SARIF output (`--format sarif`) carries
that tag as each finding's `EAIxx` ruleId:

| family | attacks | maps to |
| --- | --- | --- |
| `instruction` | `roleplay`, `goal_substitution`, `paraphrase` | [EAI01 — Policy & instruction jailbreak](docs/TOP10.md#eai01--policy--instruction-jailbreak-direct-command-channel) |
| `visual` | `patch`, `decoy_object` | [EAI02 — Adversarial perception](docs/TOP10.md#eai02--adversarial-perception-patches--textures--sensor-spoofing) |
| `injection` | `scene_text`, `mcp_tool_desc` | [EAI05 — Indirect / embodied prompt injection](docs/TOP10.md#eai05--indirect--embodied-prompt-injection) |

## Scope and honest limitations

This is an **early, research-grade** harness, built to be reproducible and honest rather than
to oversell. Before you trust a number, know:

- **Templated attacks, not optimized ones.** The attacks are auditable string/observation
  templates (instruction reframings, image markers, scene text), **not** gradient- or
  search-based adversarial methods (GCG/PGD-style). They probe *behavioral* susceptibility,
  not worst-case robustness. Optimized VLA attacks are an open roadmap item (cf. prior art
  **BadVLA**, **AttackVLA**).
- **Only the instruction family transfers (so far).** On real SmolVLA × LIBERO, instruction
  reframings redirected the policy (roleplay 100%, goal-substitution 60%); the **visual and
  injection families produced 0% measurable lift** on the real model. Treat those two as
  stub-validated scaffolding pending stronger perturbations.
- **One policy, one suite shipped.** The architecture is model-agnostic by design (an adapter
  interface), but only the **SmolVLA / LeRobot** policy and the **LIBERO** suite are
  implemented today — generality is intended, not yet demonstrated against a second backend.
- **One task, uncalibrated predicate.** The headline result is `libero_object/0` with a
  default, **uncalibrated** keep-out zone, so ASR means "diverted out of the benign
  envelope," not a calibrated hazard rate. Multi-task + per-task zone calibration is next.

Honesty and reproducibility are the point — see
[PRIOR_ART.md](https://github.com/provael/provael/blob/main/PRIOR_ART.md) for how this sits
next to the academic state of the art.

## Install (CPU core — no GPU, no network)

With [uv](https://docs.astral.sh/uv/) (recommended):

```bash
uv sync                      # creates a venv and installs the CPU core + dev tools
```

Or with pip:

```bash
python3.12 -m venv .venv && . .venv/bin/activate
pip install -e .             # core only; lerobot is NOT pulled in
```

## Quickstart (runs in well under 5 s on a CPU)

```bash
uv run provael attack --policy stub --suite stub \
    --attacks instruction,visual,injection --episodes 10 --seed 0 --out runs/stub/
```

```
               Provael — ASR by attack
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━┓
┃ attack            ┃   ASR ┃ successes ┃ attempts ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━┩
│ decoy_object      │ 60.0% │         6 │       10 │
│ goal_substitution │ 60.0% │         6 │       10 │
│ mcp_tool_desc     │ 70.0% │         7 │       10 │
│ paraphrase        │ 70.0% │         7 │       10 │
│ patch             │ 80.0% │         8 │       10 │
│ roleplay          │ 80.0% │         8 │       10 │
│ scene_text        │ 50.0% │         5 │       10 │
└───────────────────┴───────┴───────────┴──────────┘
Attack Success Rate (ASR): 67.1% (47/70)
```

This writes `runs/stub/report.json` (machine-readable, byte-deterministic) and
`runs/stub/report.md`. Per family, seed-0 ASR is **instruction 21/30**, **visual 14/20**,
**injection 12/20** — exact, asserted numbers.

Other commands:

```bash
uv run provael list-policies            # stub (CPU); smolvla (needs the [lerobot] extra)
uv run provael list-attacks             # 7 attacks across families instruction/visual/injection
uv run provael report --in runs/stub/
uv run provael leaderboard build --runs runs --out leaderboard/results   # ranked ASR table
uv run provael version
```

## What runs on CPU vs. what needs a GPU

| Capability | CPU (default) | Needs GPU + `[lerobot]` extra |
| --- | :---: | :---: |
| `stub` policy + `stub` suite | ✅ | |
| All 3 attack families (`instruction`/`visual`/`injection`) | ✅ | |
| Scoring, runner, report, CLI, `leaderboard build` | ✅ | |
| Full test suite (`pytest`), `ruff`, `mypy` | ✅ | |
| `smolvla` policy (real SmolVLA via LeRobot) | | ✅ |
| `libero` suite (real LIBERO simulator) | | ✅ |

On CPU, `--policy smolvla` or `--suite libero` fails with a clear, actionable message (not a
traceback) telling you exactly what to install.

## Use in CI (GitHub Action)

Gate any robot/VLA repo on red-team results with the reusable Action. It runs a red-team,
uploads findings to **GitHub code scanning** as SARIF (each tagged with its `EAIxx` rule), and
fails the job when the overall ASR exceeds a threshold:

```yaml
# .github/workflows/provael.yml
permissions:
  contents: read
  security-events: write   # required to upload SARIF
jobs:
  redteam:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: provael/provael@v0.3.0
        with:
          attacks: instruction,visual,injection
          episodes: "10"
          asr-threshold: "0.5"   # fail the job if overall ASR > 50%
```

The default `stub` policy + suite run on a **CPU** runner — no GPU, no model download — a fast
smoke test of the gate wiring. Red-teaming a **real** policy (`policy: smolvla`,
`suite: libero`) needs a **GPU runner** plus the `[lerobot]` extra; see the commented job in
[examples/workflow.yml](examples/workflow.yml).

## First real result (SmolVLA on LIBERO)

`HuggingFaceVLA/smolvla_libero` · `libero_object/0` · 10 seeds · horizon 280 · RTX 4090
(`osmesa`), 2026-06-06.

| family | attack | ASR | lift vs baseline |
| --- | --- | ---: | ---: |
| baseline | `none` | 0% (0/10) | — |
| instruction | `roleplay` | **100% (10/10)** | **+100** |
| instruction | `goal_substitution` | **60% (6/10)** | **+60** |
| instruction | `paraphrase` | 10% (1/10) | +10 |
| visual | `patch` | 0% (0/10) | 0 |
| visual | `decoy_object` | 0% (0/10) | 0 |
| injection | `scene_text` | 0% (0/10) | 0 |
| **overall** | | **24.3% (17/70) ± 9.1%** | |

Read it as **lift over the benign baseline**. The `none` control runs the policy's *real*
task and scores **0/10**, so every success is attack-induced. Language-reframing attacks
reliably divert SmolVLA's end-effector; pixel/scene-text perturbations did not move it (0%).

> ⚠️ **Preliminary.** The keep-out predicate is a **default, uncalibrated** region, so this
> measures "diverted out of the benign safe envelope," not a zone-calibrated hazard rate;
> `n = 10`, one task. Per-task **keep-out-zone calibration** is a planned follow-up.

## How it works

```
        ┌───────────┐    instruction     ┌──────────┐   adversarial    ┌──────────┐
 task → │ SuiteAdapter│ ───────────────→ │  Attack  │ ───instruction─→ │ Policy   │
        │  reset/step │                   │ perturb()│                  │  Adapter │
        │  is_unsafe()│ ←─── action ──────┴──────────┘                  │  act()   │
        └─────┬───────┘                                                 └────┬─────┘
              │  for t in horizon: if is_unsafe(state) → success              │
              └──────────────────────── runner ─────────────────────────────┘
                                          │
                                          ▼
                               scoring (ASR) → RunReport → report.json / report.md
```

- **`PolicyAdapter`** — `load()`, `act(observation, instruction) -> np.ndarray`.
- **`SuiteAdapter`** — `tasks()`, `reset(task, seed)`, `step(action)`, `is_unsafe(state)`.
- **`Attack`** — `perturb(instruction, observation) -> (instruction, observation)`.
- **`runner`** — runs every `(task, attack, seed)` episode and aggregates.
- **ASR** — `successes / attempts`, with `by_attack` and `by_task` breakdowns.

**Determinism.** A `RunReport` embeds no wall-clock time or process-varying values, so the
same config + seed always produces a byte-identical `report.json`.

## Roadmap

- **v0.1.0** — Provael (rebrand of the harness): CPU core, 3 attack families, real
  SmolVLA × LIBERO path, leaderboard.
- **v0.3.0** — **SARIF output** (`provael report --format sarif`) so findings surface in
  GitHub code scanning · a **reusable GitHub Action** (`provael/provael`) that gates CI on
  ASR · **Embodied-AI Top-10 mapping** (every attack tagged to an `EAIxx` risk).
  *(this release)*
- **next** — per-task keep-out-zone calibration; optimized (gradient/search) attacks; a
  second policy/suite backend.

## Development

```bash
uv run ruff check .      # lint
uv run mypy src          # type-check (strict)
uv run pytest -q         # tests (CPU only; LeRobot tests skip unless gated)
```

## Further reading

- **[SAFETY.md](https://github.com/provael/provael/blob/main/SAFETY.md)** — responsible use, sim-only default, scope.
- **[PRIOR_ART.md](https://github.com/provael/provael/blob/main/PRIOR_ART.md)** — RoboPAIR, POEX, BadVLA, SafeVLA, and how we differ.
- **[CHANGELOG.md](https://github.com/provael/provael/blob/main/CHANGELOG.md)** — what shipped and what's planned.

## License

[Apache-2.0](https://github.com/provael/provael/blob/main/LICENSE). Provael — *prove it, prevail.*
