Spaces:
Running
Running
File size: 3,182 Bytes
877add7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | """Anti reward-hacking guards."""
from __future__ import annotations
from collections import Counter
from dataclasses import dataclass
from typing import Iterable
from app.common.constants import MAX_KEEP_REGIMEN_RATIO, MAX_REPEATED_ACTIONS, MAX_REVIEW_RATIO
from app.common.enums import ActionType
from app.common.types import PolyGuardAction, PolyGuardState
@dataclass(slots=True)
class AntiCheatResult:
exploit_detected: bool
reasons: list[str]
def detect_repeated_action_loop(actions: Iterable[PolyGuardAction], threshold: int = 3) -> bool:
ids = [a.candidate_id for a in actions]
if len(ids) < threshold:
return False
return len(set(ids[-threshold:])) == 1
def evaluate_anti_cheat(
state: PolyGuardState,
action: PolyGuardAction,
legal_candidate_ids: set[str] | None = None,
) -> AntiCheatResult:
reasons: list[str] = []
history = [
PolyGuardAction.model_validate(item["action"]) if isinstance(item.get("action"), dict) else None
for item in state.action_history
]
history = [x for x in history if x is not None]
if detect_repeated_action_loop(history + [action], threshold=MAX_REPEATED_ACTIONS):
reasons.append("repeated_action_loop")
action_types = [a.action_type for a in history]
type_count = Counter(action_types)
keep_count = type_count.get(ActionType.KEEP_REGIMEN, 0) + (1 if action.action_type == ActionType.KEEP_REGIMEN else 0)
total_count = len(history) + 1
if total_count >= 3 and (keep_count / total_count) > MAX_KEEP_REGIMEN_RATIO:
reasons.append("keep_regimen_abuse")
review_actions = {
ActionType.REQUEST_SPECIALIST_REVIEW,
ActionType.REQUEST_PHARMACIST_REVIEW,
}
review_count = sum(1 for t in action_types if t in review_actions) + (1 if action.action_type in review_actions else 0)
if total_count >= 3 and (review_count / total_count) > MAX_REVIEW_RATIO:
reasons.append("review_abuse")
if not action.candidate_id.startswith("cand_"):
reasons.append("candidate_id_mismatch")
if legal_candidate_ids is not None and action.candidate_id not in legal_candidate_ids:
reasons.append("candidate_not_in_legal_set")
# Hidden holdout rule: known high-risk pair should not be repeatedly ignored.
risky_pair_key = {"warfarin_like", "nsaid_like"}
current_drugs = {m.drug for m in state.patient.medications}
if risky_pair_key.issubset(current_drugs) and action.action_type == ActionType.KEEP_REGIMEN:
reasons.append("holdout_ddi_not_addressed")
if "<" in action.rationale_brief or "{" in action.rationale_brief:
reasons.append("parser_exploit_pattern")
if state.action_history:
last = state.action_history[-1]
last_action = last.get("action", {})
if (
isinstance(last_action, dict)
and last_action.get("candidate_id") == action.candidate_id
and last_action.get("action_type") == action.action_type.value
and last.get("applied") is False
):
reasons.append("no_op_retry_loop")
return AntiCheatResult(exploit_detected=bool(reasons), reasons=reasons)
|