Add Space configuration

README.md

@@ -1,202 +1,12 @@
-# LLM Prompt Injection Security Evaluation Framework
-
-This repository contains the capstone framework for evaluating prompt injection
-attacks against LLM systems, applying mitigations, and comparing security
-outcomes before and after defense.
-
-## Project State
-
-The current branch reflects a usable end-to-end research framework:
-
-- a stable schema for datasets, configs, and run artifacts
-- an active normalized corpus built from HackAPrompt and TensorTrust
-- runnable model adapters for `mock`, `groq`, and `ollama`
-- category-aware evaluation and scoring
-- a mitigation layer integrated into the runner
-- baseline-vs-defended comparison workflows
-- a lightweight Streamlit dashboard for presenting results
-
-This is a capstone evaluation framework, not a production security product.
-
-## Scope
-
-### In scope
-
-- attack scenario design and normalization
-- LLM vulnerability evaluation
-- mitigation implementation
-- comparative measurement and reporting
-
-### Out of scope
-
-- training new foundation models
-- building a consumer chatbot product
-- large-scale model training infrastructure
-
-## Repository Structure
-
-- `src/prompt_injection_framework/attacks/`: attack logic and generators
-- `src/prompt_injection_framework/mitigations/`: request and response defenses
-- `src/prompt_injection_framework/models/`: model adapters
-- `src/prompt_injection_framework/evaluation/`: scoring and metrics
-- `src/prompt_injection_framework/orchestration/`: experiment runner and config loading
-- `configs/`: runnable experiment configurations
-- `dashboard/`: Streamlit demo app
-- `tests/`: automated validation
-- `docs/`: project, methodology, and workflow documentation
-
-## Active Data State
-
-- Active normalized sources:
-  - HackAPrompt: `1,000` curated cases
-  - TensorTrust: `1,346` curated cases
-- Active corpus files:
-  - `data/normalized/attack_cases.jsonl`
-  - `data/normalized/attack_cases.enriched.jsonl`
-- Total active normalized corpus:
-  - `2,346` cases
-
-AgentDojo remains a reference source but is not part of the active experiment
-corpus in the current project state.
-
-## Supported Model Providers
-
-- `mock`
-  - for fast validation and testing
-- `groq`
-  - for hosted real-model experiments
-- `ollama`
-  - for local/open-source model experiments
-
-More detail is documented in `docs/PHASE4_BASELINE_MODELS.md`.
-
-## Execution Pipeline
-
-1. Load normalized cases
-2. Filter and sample the experiment slice
-3. Map cases into taxonomy-aware experiment records
-4. Build model requests
-5. Apply mitigation if enabled
-6. Run the model or mitigation block path
-7. Apply response mitigation
-8. Score outputs
-9. Save manifest, case results, and summary artifacts
-
-## Quick Start
-
-### Environment setup
-
-```bash
-python3 -m venv .venv
-source .venv/bin/activate
-pip install -U pip
-pip install -e .[dev]
-```
-
-### Run a baseline experiment
-
-```bash
-python -m prompt_injection_framework.orchestration.run_experiment --config configs/baseline.yaml
-```
-
-### Run tests
-
-```bash
-pytest
-```
-
-### Run the dashboard
-
-```bash
-pip install -e .[dashboard]
-streamlit run dashboard/streamlit_app.py
-```
-
-## Core Experiment Configs
-
-### Baseline and evaluation slices
-
-- `configs/active_corpus_smoke_40.yaml`
-- `configs/hackaprompt_override_25.yaml`
-- `configs/hackaprompt_leakage_20.yaml`
-- `configs/tensortrust_hijacking_25.yaml`
-- `configs/tensortrust_extraction_25.yaml`
-
-### Defended counterparts
-
-- `configs/hackaprompt_override_25_mitigated.yaml`
-- `configs/tensortrust_hijacking_25_mitigated.yaml`
-- `configs/hackaprompt_direct_10_mitigated.yaml`
-- `configs/agentdojo_tool_10_mitigated.yaml`
-- `configs/hackaprompt_adaptive_1_mitigated.yaml`
-
-### Mock validation pairs
-
-- `configs/hackaprompt_override_25_mock.yaml`
-- `configs/hackaprompt_override_25_mock_mitigated.yaml`
-- `configs/tensortrust_hijacking_25_mock.yaml`
-- `configs/tensortrust_hijacking_25_mock_mitigated.yaml`
-- `configs/hackaprompt_direct_10_mock.yaml`
-- `configs/hackaprompt_direct_10_mock_mitigated.yaml`
-- `configs/agentdojo_tool_10_mock.yaml`
-- `configs/agentdojo_tool_10_mock_mitigated.yaml`
-
-### Local/open-source baselines
-
-- `configs/ollama_llama_hackaprompt_override_25.yaml`
-- `configs/ollama_qwen_tensortrust_hijacking_25.yaml`
-
-## Mitigation Layer
-
-Supported strategies:
-
-- `none`
-- `prompt_hardening`
-- `instruction_isolation`
-- `keyword_guardrail`
-- `surface_aligned`
-
-`surface_aligned` is the main defended path for the current framework because
-it routes by attack surface and category, records mitigation behavior in the
-artifacts, and supports before-vs-after comparisons directly in the runner.
-
-More detail is documented in `docs/MITIGATION_LAYER.md`.
-
-## Evaluation and Comparison
-
-The framework currently supports:
-
-- attack success rate
-- prompt leakage tracking
-- refusal tracking
-- benign utility and false-positive reporting
-- category, surface, and source breakdowns
-- baseline-vs-defended comparison outputs in JSON and Markdown
-
-Useful documentation:
-
-- `docs/PHASE5_BASELINE_EVALUATION.md`
-- `docs/PHASE7_EXPERIMENT_GUIDE.md`
-- `docs/GROQ_RUNBOOK.md`
-- `docs/RESULTS_TEMPLATE.md`
-
-## Dashboard
-
-The repository includes a lightweight Streamlit dashboard that reads the saved
-experiment artifacts and presents:
-
-- run summaries
-- baseline-vs-defended comparisons
-- category, surface, and source breakdowns
-- case-level inspection views
-
-See `docs/DASHBOARD_GUIDE.md`.
-
-## Notes
-
-- The schema contract is documented in `docs/SCHEMA_CONTRACT.md`.
-- The taxonomy contract is documented in `docs/ATTACK_TAXONOMY.md`.
-- Success criteria are documented in `docs/SUCCESS_CRITERIA.md`.
-- The current system is strong enough for capstone experiments, but real-model
-  evaluation still shows that some benchmark slices benefit from additional
-  mitigation refinement.
+---
+title: G5 Eval Dashboard
+emoji: 🛡️
+colorFrom: blue
+colorTo: indigo
+sdk: docker
+pinned: false
+---
+
+# G5 Eval Dashboard
+
+Prompt-injection security evaluation dashboard.