scripts/baseline_agent.py

#!/usr/bin/env python3
"""
IAMSentinel Baseline Inference Script
======================================
Runs a GPT-4o ReAct agent against all 3 tasks and reports scores.

Usage:
    export OPENAI_API_KEY=sk-...
    python scripts/baseline_agent.py [--task all|task1|task2|task3] [--seed 42] [--model gpt-4o]

Reproducible baseline scores (seed=42, complexity=medium, model=gpt-4o-mini):
    Task 1 (Easy):   ~0.55–0.70
    Task 2 (Medium): ~0.35–0.50
    Task 3 (Hard):   ~0.20–0.35
"""

import argparse
import json
import os
import sys
import time
from typing import Optional

try:
    from openai import OpenAI
except ImportError:
    print("ERROR: openai package not installed. Run: pip install openai")
    sys.exit(1)

# Ensure package is importable
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from iamsentinel import IAMSentinelEnv


# ──────────────────────────────────────────────
# System prompt for the ReAct agent
# ──────────────────────────────────────────────

SYSTEM_PROMPT = """You are an expert cloud security analyst specialising in AWS IAM security.
You are operating inside a simulated IAM environment and must complete security tasks.

You interact with the environment by outputting JSON actions. Each response must contain
EXACTLY ONE action as a JSON block in this format:

```json
{
  "action": "<action_name>",
  ... action parameters ...
}
```

Available actions:
1. list_principals — {"action": "list_principals", "kind": "all"|"user"|"role"}
2. list_policies   — {"action": "list_policies", "principal_arn": "<arn or null>"}
3. get_policy      — {"action": "get_policy", "policy_arn": "<arn>"}
4. get_principal   — {"action": "get_principal", "principal_arn": "<arn>"}
5. get_role_trust  — {"action": "get_role_trust", "role_arn": "<arn>"}
6. query_audit_log — {"action": "query_audit_log", "filter": {"event_name": "...", "severity": "...", "principal_arn": "...", "source_ip": "..."}, "limit": 20}
7. trace_escalation_path — {"action": "trace_escalation_path", "from_principal_arn": "<arn>", "to_principal_arn": null}
8. flag_finding    — {
     "action": "flag_finding",
     "finding_type": "wildcard_policy"|"mfa_disabled"|"stale_admin_role"|"privilege_escalation_path"|"exposed_trust_policy"|"suspicious_event",
     "affected_principal_arn": "<arn or null>",
     "affected_policy_arn": "<arn or null>",
     "severity": "low"|"medium"|"high"|"critical",
     "description": "<description>",
     "mitre_technique": "<T-code or null>",
     "evidence": ["<arn or event_id>", ...]
   }
9. remediate       — {"action": "remediate", "remediation_type": "detach_policy"|"delete_user"|"require_mfa"|"update_trust_policy", "target_arn": "<arn>", "policy_arn": "<arn or null>"}
10. attribute_attack — {
      "action": "attribute_attack",
      "compromised_principal_arn": "<arn>",
      "attack_technique": "<description>",
      "mitre_techniques": ["T1078.004", ...],
      "lateral_movement_path": ["<arn1>", "<arn2>"],
      "containment_actions": ["disable_user:<arn>", "delete_function:<name>", ...]
    }

Strategy guidelines:
- For Task 1: List all principals and their policies. Check for wildcards, MFA, stale roles, exposed trust policies.
- For Task 2: Find principals with iam:PassRole. Trace escalation paths. Look for lambda + createUser chains.
- For Task 3: Query audit logs by severity=critical first, then trace suspicious sequences. Look for CreateFunction→CreateUser chains from unusual IPs.

Be systematic. Think step by step before each action. Flag findings as you discover them.
For Task 3, finish with attribute_attack once you've gathered enough evidence.
"""


# ──────────────────────────────────────────────
# JSON action parser
# ──────────────────────────────────────────────

def extract_json_action(text: str) -> Optional[dict]:
    """Extract the first JSON block from model output."""
    import re
    # Try fenced code block first
    pattern = r"```(?:json)?\s*(\{.*?\})\s*```"
    match = re.search(pattern, text, re.DOTALL)
    if match:
        try:
            return json.loads(match.group(1))
        except json.JSONDecodeError:
            pass

    # Try raw JSON
    pattern2 = r"\{[^{}]*\"action\"[^{}]*\}"
    match2 = re.search(pattern2, text, re.DOTALL)
    if match2:
        try:
            return json.loads(match2.group(0))
        except json.JSONDecodeError:
            pass

    # Try to find largest JSON object
    for start in range(len(text)):
        if text[start] == "{":
            for end in range(len(text), start, -1):
                if text[end-1] == "}":
                    try:
                        obj = json.loads(text[start:end])
                        if "action" in obj:
                            return obj
                    except json.JSONDecodeError:
                        continue
    return None


def obs_to_text(obs_dict: dict, step: int) -> str:
    """Convert observation dict to a concise text summary for the LLM."""
    parts = [f"[Step {step}] Budget remaining: {obs_dict.get('budget_remaining', '?')}"]

    if obs_dict.get("hints"):
        parts.append("Hints: " + " | ".join(obs_dict["hints"]))

    if obs_dict.get("findings"):
        parts.append(f"Findings so far ({len(obs_dict['findings'])}):")
        for f in obs_dict["findings"][-3:]:  # last 3
            parts.append(f"  - [{f['severity']}] {f['finding_type']}: {f['description'][:80]}")

    if obs_dict.get("principals"):
        parts.append(f"Principals returned: {len(obs_dict['principals'])}")
        for p in obs_dict["principals"][:5]:
            mfa = "✓MFA" if p.get("mfa_enabled") else "✗MFA"
            parts.append(
                f"  {p['kind']}: {p['name']} | {mfa} | "
                f"last_active={p['last_active_days']}d | "
                f"policies={len(p.get('policies', []))}"
            )
        if len(obs_dict["principals"]) > 5:
            parts.append(f"  ... and {len(obs_dict['principals'])-5} more")

    if obs_dict.get("policies"):
        parts.append(f"Policies returned: {len(obs_dict['policies'])}")
        for p in obs_dict["policies"][:5]:
            wildcard = "⚠WILDCARD" if p.get("is_wildcard") else ""
            parts.append(f"  {p['name']} {wildcard} | arn={p['arn']}")
            if p.get("statements"):
                actions = p["statements"][0].get("actions", [])
                parts.append(f"    actions: {actions[:5]}")
        if len(obs_dict["policies"]) > 5:
            parts.append(f"  ... and {len(obs_dict['policies'])-5} more")

    if obs_dict.get("audit_events"):
        parts.append(f"Audit events returned: {len(obs_dict['audit_events'])}")
        for e in obs_dict["audit_events"][:8]:
            parts.append(
                f"  [{e.get('severity','?')}] {e['event_time']} | "
                f"{e['event_name']} | {e['principal_name']} | ip={e['source_ip']}"
            )
        if len(obs_dict["audit_events"]) > 8:
            parts.append(f"  ... and {len(obs_dict['audit_events'])-8} more")

    if obs_dict.get("escalation_paths"):
        parts.append(f"Escalation paths found: {len(obs_dict['escalation_paths'])}")
        for ep in obs_dict["escalation_paths"][:3]:
            parts.append(f"  Path (risk={ep.get('risk_score','?')}): {' → '.join(ep['path'])}")

    if obs_dict.get("role_trust_policy"):
        parts.append(f"Trust policy: {json.dumps(obs_dict['role_trust_policy'], indent=2)[:300]}")

    if obs_dict.get("done"):
        parts.append("EPISODE DONE.")

    return "\n".join(parts)


# ──────────────────────────────────────────────
# Agent runner
# ──────────────────────────────────────────────

def run_agent(
    task_id: str,
    seed: int = 42,
    model: str = "gpt-4o-mini",
    complexity: str = "medium",
    verbose: bool = True,
) -> dict:
    api_key = os.environ.get("OPENAI_API_KEY")
    if not api_key:
        raise ValueError("OPENAI_API_KEY environment variable not set")

    client = OpenAI(api_key=api_key)
    env = IAMSentinelEnv(task_id=task_id, seed=seed, complexity=complexity)
    obs = env.reset()

    task_cfg = {
        "task1": {"name": "Misconfiguration Scanner",          "difficulty": "Easy"},
        "task2": {"name": "Privilege Escalation Path Detection","difficulty": "Medium"},
        "task3": {"name": "Live Attack Attribution",           "difficulty": "Hard"},
    }[task_id]

    if verbose:
        print(f"\n{'='*60}")
        print(f"Task: {task_cfg['name']} ({task_cfg['difficulty']})")
        print(f"Seed: {seed} | Model: {model} | Complexity: {complexity}")
        print(f"{'='*60}")

    # Build conversation history
    messages = [{"role": "system", "content": SYSTEM_PROMPT}]

    # Initial user message with task description
    initial_msg = (
        f"Task: {obs.task_description}\n\n"
        f"Account ID: {obs.account_id}\n"
        f"Max steps: {obs.max_steps}\n"
    )
    if obs.hints:
        initial_msg += "\nHints:\n" + "\n".join(f"- {h}" for h in obs.hints)
    initial_msg += "\n\nBegin your investigation. Output one JSON action."

    messages.append({"role": "user", "content": initial_msg})

    episode_done = False
    step = 0
    final_score = 0.0
    total_reward = 0.0
    action_history = []

    while not episode_done and step < env._max_steps():
        step += 1

        # ── Call LLM ──────────────────────────
        try:
            response = client.chat.completions.create(
                model=model,
                messages=messages,
                temperature=0.2,
                max_tokens=800,
            )
            assistant_text = response.choices[0].message.content
        except Exception as e:
            print(f"  [Step {step}] LLM error: {e}")
            time.sleep(2)
            continue

        messages.append({"role": "assistant", "content": assistant_text})

        # ── Parse action ───────────────────────
        action_dict = extract_json_action(assistant_text)
        if action_dict is None:
            if verbose:
                print(f"  [Step {step}] Could not parse action from: {assistant_text[:100]}")
            feedback = "ERROR: Could not parse a valid JSON action. Output ONLY a JSON block."
            messages.append({"role": "user", "content": feedback})
            continue

        action_name = action_dict.get("action", "unknown")
        action_history.append(action_name)

        if verbose:
            print(f"  [Step {step}] Action: {action_name}", end="")
            key_params = {k: v for k, v in action_dict.items()
                         if k != "action" and v is not None}
            if key_params:
                print(f" | params: {json.dumps(key_params)[:100]}", end="")
            print()

        # ── Step environment ───────────────────
        try:
            next_obs, reward, done, info = env.step(action_dict)
        except Exception as e:
            feedback = f"ERROR executing action: {e}. Try a different action."
            messages.append({"role": "user", "content": feedback})
            continue

        total_reward += reward.total
        episode_done = done

        if done and info.get("final_score") is not None:
            final_score = info["final_score"]
            if verbose:
                print(f"  [Step {step}] Episode done. Final score: {final_score:.3f}")

        # ── Build feedback message ─────────────
        obs_dict = next_obs.model_dump()
        feedback_text = obs_to_text(obs_dict, step)
        if reward.step_reward != 0:
            feedback_text += f"\n[Reward signal: {reward.step_reward:+.3f}]"
        if obs_dict.get("findings"):
            feedback_text += f"\n[Total findings logged: {len(obs_dict['findings'])}]"

        if not done:
            feedback_text += "\n\nContinue your investigation. Output one JSON action."

        messages.append({"role": "user", "content": feedback_text})

        # Small delay to respect rate limits
        time.sleep(0.3)

    return {
        "task_id":       task_id,
        "task_name":     task_cfg["name"],
        "difficulty":    task_cfg["difficulty"],
        "seed":          seed,
        "model":         model,
        "final_score":   final_score,
        "total_reward":  total_reward,
        "steps_taken":   step,
        "action_history":action_history,
        "state":         env.state(),
    }


# ──────────────────────────────────────────────
# Main entry point
# ──────────────────────────────────────────────

def main():
    parser = argparse.ArgumentParser(description="IAMSentinel Baseline Agent")
    parser.add_argument("--task",       default="all",      help="task1|task2|task3|all")
    parser.add_argument("--seed",       type=int, default=42)
    parser.add_argument("--model",      default="gpt-4o-mini")
    parser.add_argument("--complexity", default="medium",   help="easy|medium|hard")
    parser.add_argument("--output",     default=None,       help="Save results to JSON file")
    parser.add_argument("--quiet",      action="store_true")
    args = parser.parse_args()

    tasks = ["task1", "task2", "task3"] if args.task == "all" else [args.task]
    results = []

    for task_id in tasks:
        result = run_agent(
            task_id=task_id,
            seed=args.seed,
            model=args.model,
            complexity=args.complexity,
            verbose=not args.quiet,
        )
        results.append(result)

    # ── Print summary ──────────────────────────
    print("\n" + "="*60)
    print("BASELINE SCORES SUMMARY")
    print("="*60)
    print(f"{'Task':<35} {'Score':>6}  {'Steps':>5}  {'Difficulty'}")
    print("-"*60)
    for r in results:
        print(
            f"{r['task_name']:<35} {r['final_score']:>6.3f}  "
            f"{r['steps_taken']:>5}  {r['difficulty']}"
        )
    print("-"*60)
    avg = sum(r["final_score"] for r in results) / len(results)
    print(f"{'Average':<35} {avg:>6.3f}")
    print("="*60)

    if args.output:
        with open(args.output, "w") as f:
            json.dump(results, f, indent=2)
        print(f"\nResults saved to {args.output}")

    return results


if __name__ == "__main__":
    main()