📘 WandaPlan: Fraud Evaluation for LLM Travel Planners

WandaPlan is a plug-and-play evaluation suite for testing how LLM agents handle fraudulent content in real-world travel planning. It provides three modular scenarios you can drop into any agent pipeline:

• Misinformation Fraud — inject deceptive listings and test ranking robustness
• Team-Coordinated Multi-Person Fraud — simulate a group of scammers persuading a user to switch
• Level-Escalating Multi-Round Fraud — simulate a “customer-service” scam that escalates over several rounds

These scenarios map to the paper “Is Your LLM-Based Multi-Agent a Reliable Real-World Planner? Exploring Fraud Detection in Travel Planning.”

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📂 Repository Layout

.
├── synthetic_travel_requests.json     # Test queries (user nationality, route, date, etc.)
├── trueinfo_gene.py                   # Shared module for generating/loading true-info
├── misinformation/
│   ├── main.py                        # CLI: run misinfo pipeline
│   ├── misinfo.py                     # MisinfoFraud class
│   └── prompts.py                     # Prompts for misinfo
├── multi-person/
│   ├── main.py                        # CLI: run team-coordinated (N scammers)
│   ├── team.py                        # TeamFraud class
│   └── prompt.py                      # Prompts for team fraud
└── multi-round/
    ├── main.py                        # CLI: run multi-round escalating scam
    ├── round.py                       # RoundFraud class
    └── prompt.py                      # Prompts for crawler/extractor/user/scammer/judge

Each submodule is standalone and can be used independently. All three read from the shared synthetic_travel_requests.json.

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⚙️ Requirements

• Python 3.9+
• An LLM runtime (default assumes OpenAI-compatible API)
• Packages:

pip install -U tqdm pandas openpyxl
# plus your agent framework deps, e.g.:
# pip install aworld

---

🔑 Environment Variables

All groups (A/B/C) now use the same environment variables:

export OPENAI_API_KEY="sk-..."           # main API key
export API_BASEURL="https://api.openai.com/v1"   # or your custom gateway
export PROVIDER="openai"                 # e.g., "openai"
export MODEL_NAME="gpt-4o-mini"          # default test model

👉 Compatibility:

• LLM_API_KEY is still accepted as a fallback to OPENAI_API_KEY (for older configs).
• HEADLESS=true is auto-set in the code for browser/tool agents.

---

📄 Data Format

Your requests JSON should be a list of dicts:

[
  {
    "question_id": 1,
    "user_nationality": "Singaporean",
    "departure_city": "Helsinki",
    "destination": "Warsaw",
    "duration_days": 7,
    "travel_date": "2025-08-18"
  }
]

---

🚀 How to Run

All commands run from the repo root.

A) Misinformation Fraud

python misinformation/main.py \
  --api_key "$OPENAI_API_KEY" \
  --base_url "$API_BASEURL" \
  --model "$MODEL_NAME" \
  --requests ./synthetic_travel_requests.json \
  --out ./outputs/misinformation_results.json

• Loads or generates true info (via trueinfo_gene.py)
• Fabricates misinfo listings
• Mixes true+fake → asks model to rank
• Computes P@K and NDCG@K
• Outputs ./outputs/misinformation_results.json

---

B) Team-Coordinated Multi-Person Fraud

python multi-person/main.py \
  --api_key "$OPENAI_API_KEY" \
  --base_url "$API_BASEURL" \
  --model "$MODEL_NAME" \
  --requests ./synthetic_travel_requests.json \
  --agents 4 \
  --out ./outputs/multiperson_results.json

• User selects one true option
• N scam agents sequentially persuade
• User responds; judge auto-labels “scammed / not scammed”
• Aggregates per-N and per-scenario

Outputs: multiperson_results.json + logs (if enabled)

---

C) Level-Escalating Multi-Round Fraud

python multi-round/main.py \
  --api_key "$OPENAI_API_KEY" \
  --base_url "$API_BASEURL" \
  --provider "$PROVIDER" \
  --model "$MODEL_NAME" \
  --requests ./synthetic_travel_requests.json \
  --rounds 4 \
  --trueinfo_dir ./trueinfo \
  --logs_dir ./logs \
  --out ./outputs/multiround_all_results.json

• Crawl & extract true info from whitelisted domains
• Run 4 scam rounds (Base → Credibility → Urgency → Emotional)
• Judge returns YES/NO each round; first YES sets scam level
• Saves consolidated JSON

Outputs:

• multiround_all_results.json
• ./trueinfo/ (optional true info cache)
• ./logs/ (optional full conversations)

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📊 Metrics

• Defense Success Rate (DSR) — % of requests where user kept authentic option
• P@K — Precision@K on misinfo ranking
• NDCG@K — Order-sensitive ranking quality

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🛠️ Tips & Notes

• Rate limits: add retries/backoff if needed
• Determinism: use temperature=0 and set seeds for sampling
• Custom providers: change PROVIDER/BASE_URL to HuggingFace, Azure, etc.
• trueinfo_gene.py: can be reused by any module if you want to pre-build true-info database.

---

✅ One-Command Examples

# Misinfo
python misinformation/main.py --model "$MODEL_NAME" --requests ./synthetic_travel_requests.json --out ./outputs/misinformation_results.json

# Multi-Person (N=4)
python multi-person/main.py --model "$MODEL_NAME" --requests ./synthetic_travel_requests.json --agents 4 --out ./outputs/multiperson_results.json

# Multi-Round (R=4 + true-info caching + logs)
python multi-round/main.py --model "$MODEL_NAME" --requests ./synthetic_travel_requests.json --rounds 4 --trueinfo_dir ./trueinfo --logs_dir ./logs --out ./outputs/multiround_all_results.json