tasks/master-plan.md

SentinelOps Arena -- Master Improvement Plan

Created: Sunday March 8, 2026 Goal: Maximize hackathon judging score with surgical code fixes

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Priority Legend

Score	Meaning
10	Must fix -- breaks core functionality or judges will reject
8-9	High impact -- judges will directly notice and reward
5-7	Noticeable improvement -- strengthens the demo
1-4	Low impact -- skip unless time permits

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CRITICAL FIXES (Bugs that break core functionality)

FIX-1: Billing issue_refund() never checks window_ticks [Priority: 10]

Bug: billing.py:issue_refund() checks max_amount and requires_approval but NEVER checks window_ticks. Policy drift attacks that change window_ticks have zero effect on refund validation. This means 1/3 of policy drift parameters is dead code.

File: sentinelops_arena/systems/billing.py (lines 47-89) Change: Add window_ticks validation. The invoice has date_tick and the environment tracks the current tick. Pass current_tick into issue_refund() and compare current_tick - invoice["date_tick"] against self.refund_policy.window_ticks. Impact: Policy drift attacks now meaningfully change refund behavior. Patronus AI judges (schema/policy drift track) will directly verify this works. Lines of code: ~10 lines in billing.py, ~3 lines in environment.py to pass current_tick

Details:

• Add current_tick: int parameter to issue_refund()
• After the existing checks, add:

  ticks_since_invoice = current_tick - invoice.get("date_tick", 0)
  if ticks_since_invoice > self.refund_policy.window_ticks:
      return {"error": f"Refund window expired. Invoice is {ticks_since_invoice} ticks old, policy allows {self.refund_policy.window_ticks}"}
  

• Update environment.py _execute_worker_action to pass self.tick
• Update the MCP tool issue_refund to pass self.tick

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FIX-2: CRM and Ticketing have no rate limiting support [Priority: 8]

Bug: attacks.py:_execute_rate_limit() calls system.set_rate_limit(), but only BillingSystem implements it. CRMSystem and TicketingSystem have no set_rate_limit, _rate_limit, _call_count, or _rate_limit_check(). The attack manager already checks hasattr(system, "set_rate_limit") and returns error, but the attacker can still target CRM/ticketing and waste budget.

File: sentinelops_arena/systems/crm.py, sentinelops_arena/systems/ticketing.py Change: Add rate limiting to CRM and Ticketing, mirroring BillingSystem's implementation. Impact: Rate limit attacks now work on all 3 systems. The _is_rate_limited() check in environment.py (line 601-606) already handles this via hasattr(system, "_rate_limit"), so once the attribute exists, rate limiting shows up in the dashboard. Lines of code: ~20 lines per system (copy from billing.py pattern)

Details for each system (CRM + Ticketing):

• Add self._rate_limit: int = 0 and self._call_count: int = 0 to __init__
• Add _rate_limit_check() method (copy from billing.py)
• Add set_rate_limit() method (copy from billing.py)
• Add reset_rate_limit_counter() method (copy from billing.py)
• Add if self._rate_limit_check(): return {"error": "Rate limit exceeded."} to lookup_customer, update_tier, add_note, get_history, and for ticketing: create_ticket, assign_ticket, escalate, resolve, check_sla
• Update environment.py to reset CRM and ticketing counters each tick (add to the tick-advance block at line 346)

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FIX-3: Schema drift renames target non-existent fields [Priority: 7]

Bug: SCHEMA_DRIFT_RENAMES in demo.py includes {"old_field": "email", ...}, {"old_field": "address", ...}, {"old_field": "phone", ...}, {"old_field": "id", ...}. But the Customer model has fields: customer_id, name, tier, region, contact_email, lifetime_value, notes. Only name -> full_name actually works. The others silently do nothing because the fields don't exist.

File: sentinelops_arena/demo.py (lines 125-131) Change: Fix renames to use actual Customer model field names. Lines of code: ~5 lines

New renames:

SCHEMA_DRIFT_RENAMES = [
    {"old_field": "name", "new_field": "full_name"},
    {"old_field": "contact_email", "new_field": "email_address"},
    {"old_field": "region", "new_field": "territory"},
    {"old_field": "tier", "new_field": "membership_level"},
    {"old_field": "lifetime_value", "new_field": "total_spend"},
]

Also fix in train.py (lines 311-312) which has the same bad renames.

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FIX-4: tasks_completed count is always 0 [Priority: 5]

Bug: environment.py line 356-360 counts tasks_completed by checking t.get("task_completed") in trajectory entries, but no trajectory entry ever sets task_completed = True. The trajectory append at line 329-336 only stores tick, agent, action_type, reward.

File: sentinelops_arena/environment.py (lines 329-336, 356-360) Change: Add task_completed flag to trajectory entries when worker successfully completes a task. Lines of code: ~3 lines

Details:

• In the trajectory append, add "task_completed": (action.agent == AgentRole.WORKER and self.last_worker_result and self.last_worker_result.get("success", False))
• Or simpler: after computing worker reward, if result["success"], set a flag on the trajectory entry

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HIGH-IMPACT IMPROVEMENTS (Things judges will notice/reward)

IMP-1: Apply Gradio theme in gr.Blocks() constructor [Priority: 9]

Bug: The SentinelTheme() and CUSTOM_CSS are passed to demo.launch() but NOT to gr.Blocks(). On HuggingFace Spaces, launch() args may be ignored. The theme must be in the constructor.

File: app.py (line 124, line 444-448) Change: Move theme and css into gr.Blocks():

with gr.Blocks(title="SentinelOps Arena", fill_width=True, theme=SentinelTheme(), css=CUSTOM_CSS) as demo:

Remove duplicate theme/css from demo.launch(). Lines of code: 2 lines

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IMP-2: Worker heuristic should complete multi-step tasks [Priority: 8]

Bug: The trained HeuristicWorker._trained_act() in demo.py checks policy for refund tasks but NEVER actually issues the refund. It just calls get_current_policy every time it sees a refund task. Same for untrained worker -- it issues refund but never checks CRM first.

File: sentinelops_arena/demo.py (lines 253-299) Change: Add state tracking to HeuristicWorker so it can complete multi-step flows:

1. First encounter of refund task: call get_current_policy
2. Second encounter (same task): call issue_refund with validated params

This will make the trained vs untrained comparison dramatically more interesting in the demo.

Lines of code: ~25 lines

Details:

• Add self._last_task_id and self._policy_checked state to HeuristicWorker
• Trained flow: refund task first seen -> get_current_policy, refund task second time -> issue_refund with compliant params
• This creates visible "adaptive behavior" in the replay -- exactly what judges want to see

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IMP-3: Improve explanation quality metric [Priority: 6]

Bug: environment.py line 441: explanation_quality = min(len(explanation) / 100.0, 1.0) -- quality is just string length. A 100+ character explanation always gets max quality regardless of content.

File: sentinelops_arena/environment.py (line 441) Change: Add keyword detection alongside length. Check if explanation mentions relevant terms (policy, schema, drift, social engineering, violation, refund, etc.). Lines of code: ~8 lines

keywords = ["policy", "schema", "drift", "violation", "social", "engineering",
            "refund", "unauthorized", "error", "compliance"]
keyword_matches = sum(1 for k in keywords if k in explanation.lower())
length_score = min(len(explanation) / 100.0, 0.5)
keyword_score = min(keyword_matches / 3.0, 0.5)
explanation_quality = length_score + keyword_score

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QUICK WINS (Small effort, visible improvement)

QW-1: Fix HF Spaces requirements [Priority: 9]

File: requirements.txt Change: Ensure pandas>=2.0 is listed. Verify gradio version consistency. Lines of code: 1-2 lines

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QW-2: Fix version claims in SENTINELOPS_ARENA.md [Priority: 4]

Bug: Spec says "80 ticks" and "OpenEnv 0.4" but code uses 30 ticks and OpenEnv 0.2.x. Action: SKIP -- spec docs are aspirational. Judges who read code will see it works. Not worth the time.

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QW-3: Clean up hackathon_env/ vestigial directory [Priority: 3]

File: .gitignore or delete hackathon_env/ Action: SKIP unless doing final cleanup -- judges won't look here.

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SKIP LIST (Not worth the time)

Item	Why Skip
Compound attacks	2+ hours, spec feature not in code
Compliance drift	New attack type, 1+ hour to implement and test
A2A protocol	Already marked "Cut" in spec, correct decision
Docker support	HF Spaces uses Gradio SDK
SLA breach detection	Needs rework of ticketing + reward pipeline
MCP-X gateway	MCP tools work inline, gateway is polish
Full GRPO convergence	Training pipeline exists, convergence not needed

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IMPLEMENTATION ORDER

Execute in this exact order to maximize impact per minute:

#	Item	Est. Time	Impact
1	IMP-1: Theme in gr.Blocks constructor	2 min	HF Spaces theme works
2	QW-1: Fix requirements.txt	2 min	HF Spaces doesn't crash
3	FIX-1: window_ticks enforcement in billing	10 min	Policy drift attacks work (Patronus AI track)
4	FIX-3: Fix schema drift renames	5 min	Schema drift attacks work (Patronus AI track)
5	FIX-2: Rate limiting for CRM + Ticketing	15 min	All attacks work on all systems
6	FIX-4: tasks_completed tracking	3 min	Dashboard shows correct count
7	IMP-2: Worker multi-step task completion	15 min	Demo shows real adaptive behavior
8	IMP-3: Better explanation quality metric	5 min	Oversight agent more realistic

Total estimated time: ~57 minutes

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JUDGE-SPECIFIC IMPACT ANALYSIS

Patronus AI (Darshan Deshpande) -- Schema Drift Track ($10K)

• FIX-1 makes policy drift mechanically functional (window_ticks enforced)
• FIX-3 makes schema drift renames target real fields (attacks actually break things)
• IMP-2 shows worker adapting to drift in multi-step tasks
• Combined: these 3 fixes transform "drift is mentioned" into "drift demonstrably works"

Fleet AI (Nicolai Ouporov) -- Scalable Oversight Track ($10K)

• IMP-3 gives oversight meaningful explanation quality scoring (not just string length)
• IMP-2 creates real violations for oversight to catch (multi-step tasks that can fail)
• FIX-4 shows accurate task completion stats in dashboard

Daniel Han (Unsloth) -- Training Pipeline

• The training pipeline in train.py is already solid
• Fixes to the environment make the reward signals more meaningful
• GRPO reward functions already correctly shaped

Sanyam Bhutani (Meta) -- OpenEnv Quality

• FIX-1 + FIX-2 demonstrate environment integrity (attacks have real effects)
• Clean MCP tool exposure with 19 tools already impressive
• Environment reset/step/state cycle works correctly

Benjamin Burtenshaw (HuggingFace) -- Hub Deployment

• IMP-1 + QW-1 ensure HF Spaces deployment works correctly with theme
• Gradio 6 native plots and custom theme are impressive

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WHAT NOT TO TOUCH

1. rewards.py -- Reward functions are clean and match spec tables. Do not modify.
2. models.py -- Pydantic models are correct. Do not add fields unless required by a fix.
3. task_generator.py -- Works fine, generates correct task mix.
4. sentinel_theme.py -- Theme is polished. Do not tweak CSS.
5. replay_html.py -- HTML rendering works. Do not modify.
6. chart_helpers.py -- Chart data builders work. Do not modify.
7. metrics.py -- Security metrics computation is solid.
8. train.py -- Only touch to fix schema_drift renames in the heuristic attacker configs.