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3807ea3 f3080d1 be8eade f3080d1 be8eade 3807ea3 be8eade 3807ea3 be8eade 3807ea3 be8eade 3807ea3 be8eade 3807ea3 6abc8c5 be8eade 6abc8c5 be8eade 6abc8c5 be8eade 3807ea3 6abc8c5 be8eade 6abc8c5 be8eade | 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 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 | """Reward computation for CyberSecurity_OWASP."""
from __future__ import annotations
try:
from .models import CyberSecurityOWASPAction, CyberSecurityOWASPState
from .reward_config import RewardSettings, load_reward_settings
except ImportError: # pragma: no cover
from models import CyberSecurityOWASPAction, CyberSecurityOWASPState
from reward_config import RewardSettings, load_reward_settings
REWARD_KEYS = (
"discovery",
"security",
"regression",
"public_routes",
"patch_quality",
"visible_tests",
"safety",
"anti_cheat",
"terminal_total",
"progressive",
"step_penalty",
"speed_bonus",
"token_penalty",
"behavior_penalty",
"train_total",
"total",
)
def empty_reward() -> dict[str, float]:
return {key: 0.0 for key in REWARD_KEYS}
def compute_reward(
state: CyberSecurityOWASPState,
action: CyberSecurityOWASPAction,
verifier_result: dict,
) -> dict[str, float]:
settings = load_reward_settings()
reward = empty_reward()
if action.tool_name == "submit_diagnosis":
diagnosis = verifier_result.get("diagnosis", verifier_result.get("finding", {}))
reward["discovery"] = _diagnosis_score(diagnosis)
elif action.tool_name == "run_visible_tests":
visible = verifier_result.get("visible", {})
reward["visible_tests"] = 1.0 if visible.get("passed") else 0.0
elif action.tool_name == "submit_fix":
_add_terminal_submit_fix_reward(state, verifier_result, reward, settings)
_add_current_anti_cheat_penalties(verifier_result, reward, settings)
if verifier_result.get("invalid_action"):
reward["behavior_penalty"] += settings.value("invalid_action", -0.2)
if verifier_result.get("repeated_action"):
reward["behavior_penalty"] += (
settings.value("repeated_invalid_action", -0.3)
if verifier_result.get("invalid_action")
else settings.value("repeated_low_value_action", -0.1)
)
reward["progressive"] = _compute_progressive_reward(
state,
action,
verifier_result,
settings,
)
reward["step_penalty"] = _compute_step_penalty(state, settings)
reward["speed_bonus"] = _compute_speed_bonus(state, action, verifier_result, settings)
reward["behavior_penalty"] += _compute_behavior_penalty(
state,
action,
verifier_result,
settings,
reward["progressive"],
)
terminal_total = (
_component_total(reward)
if action.tool_name == "submit_fix"
else reward["safety"] + reward["anti_cheat"]
)
reward["terminal_total"] = _cap_terminal(terminal_total, settings)
reward["train_total"] = _cap_train(
reward["terminal_total"]
+ settings.shaping_weight * reward["progressive"]
+ reward["step_penalty"]
+ reward["speed_bonus"]
+ reward["token_penalty"]
+ reward["behavior_penalty"],
settings,
state,
)
reward["total"] = reward["train_total"] if settings.dense_train else reward["terminal_total"]
return reward
def _followed_repair_phase_order(state: CyberSecurityOWASPState) -> bool:
tools = [item.get("tool_name") for item in state.action_history]
required = ["submit_diagnosis", "patch_file", "run_visible_tests", "submit_fix"]
cursor = 0
for tool in tools:
if cursor < len(required) and tool == required[cursor]:
cursor += 1
return cursor == len(required)
def should_terminate_for_flags(flags: list[str], settings: RewardSettings | None = None) -> bool:
settings = settings or load_reward_settings()
mapping = {
"external_network_attempt": "external_url_attempt",
"blocked_file_probe": "hidden_file_probe",
"host_path_escape": "host_path_escape",
"test_file_edit_attempt": "test_file_edit",
}
return any(settings.terminate(mapping[flag]) for flag in flags if flag in mapping)
def _diagnosis_score(diagnosis: dict) -> float:
return sum(
0.25
for key in (
"correct_route",
"correct_bug_class",
"correct_policy_rule",
"valid_local_evidence",
)
if diagnosis.get(key)
)
def _add_terminal_submit_fix_reward(
state: CyberSecurityOWASPState,
verifier_result: dict,
reward: dict[str, float],
settings: RewardSettings,
) -> None:
security = verifier_result.get("security", {})
oracle = verifier_result.get("oracle_matrix", {})
regression = verifier_result.get("regression", {})
public_routes = verifier_result.get("public_routes", {})
quality = verifier_result.get("patch_quality", {})
visible = verifier_result.get("visible", {})
security_passed = bool(security.get("passed") and oracle.get("passed", True))
reward["security"] = 5.0 if security_passed else -6.0
reward["regression"] = 3.0 if regression.get("passed") else -5.0
reward["public_routes"] = 1.0 if public_routes.get("passed") else -3.0
reward["patch_quality"] = 2.0 if quality.get("passed") else 0.0
if quality.get("passed") and _followed_repair_phase_order(state):
reward["patch_quality"] += 0.5
reward["visible_tests"] = 1.0 if visible.get("passed") else -6.0
if quality.get("hardcoded"):
reward["anti_cheat"] += settings.value("hardcoded_identifier", -2.0)
if quality.get("deny_all"):
reward["safety"] += settings.value("deny_all_patch", -3.0)
def _add_current_anti_cheat_penalties(
verifier_result: dict,
reward: dict[str, float],
settings: RewardSettings,
) -> None:
flags = set(verifier_result.get("anti_cheat_flags", []) or [])
if "external_network_attempt" in flags:
reward["safety"] += settings.value("external_url_attempt", -5.0)
if "blocked_file_probe" in flags:
reward["anti_cheat"] += settings.value("hidden_file_probe", -5.0)
if "host_path_escape" in flags:
reward["anti_cheat"] += settings.value("host_path_escape", -4.0)
if "test_file_edit_attempt" in flags:
reward["anti_cheat"] += settings.value("test_file_edit", -5.0)
if "hardcoded_hidden_identifier" in flags:
reward["anti_cheat"] += settings.value("hardcoded_identifier", -2.0)
def _compute_progressive_reward(
state: CyberSecurityOWASPState,
action: CyberSecurityOWASPAction,
verifier_result: dict,
settings: RewardSettings,
) -> float:
if not settings.dense_train:
return 0.0
delta = 0.0
if action.tool_name == "inspect_policy_graph":
delta += _award_progress_once(state, "policy_seen", "policy_inspected", settings)
if action.tool_name in {"list_routes", "read_openapi"}:
delta += _award_progress_once(state, "route_map_seen", "route_map_inspected", settings)
if action.tool_name in {"read_file", "search_code"} and _is_relevant_code_action(action):
delta += _award_progress_once(
state,
"relevant_file_seen",
"relevant_file_inspected",
settings,
)
if action.tool_name in {"send_local_request", "compare_identities"} and any(
trace.get("unauthorized_success") for trace in state.request_trace
):
delta += _award_progress_once(
state,
"local_evidence_found",
"local_evidence_found",
settings,
)
if action.tool_name == "submit_diagnosis":
diagnosis = verifier_result.get("diagnosis", verifier_result.get("finding", {}))
if all(
diagnosis.get(key)
for key in (
"correct_route",
"correct_bug_class",
"correct_policy_rule",
"valid_local_evidence",
)
):
delta += _award_progress_once(
state,
"diagnosis_correct",
"diagnosis_correct",
settings,
)
if action.tool_name == "patch_file" and not verifier_result.get("invalid_action"):
delta += _award_progress_once(state, "patch_applies", "patch_applies", settings)
if action.tool_name == "run_visible_tests":
visible = verifier_result.get("visible", {})
checks = visible.get("checks", {}) if isinstance(visible, dict) else {}
if visible.get("passed"):
delta += _award_progress_once(
state,
"app_boots",
"app_boots_after_patch",
settings,
)
delta += _award_progress_once(
state,
"visible_tests_improved",
"visible_tests_improved",
settings,
)
if checks.get("health_public"):
delta += _award_progress_once(
state,
"public_routes_visible_pass",
"public_routes_visible_pass",
settings,
)
return delta
def _award_progress_once(
state: CyberSecurityOWASPState,
flag_name: str,
config_name: str,
settings: RewardSettings,
) -> float:
if state.progress_flags.get(flag_name):
return 0.0
cap = settings.value("progressive_cap", 5.0)
remaining = max(0.0, cap - float(state.progress_reward_total or 0.0))
if remaining <= 0.0:
return 0.0
state.progress_flags[flag_name] = True
value = min(settings.value(config_name, 0.0), remaining)
state.progress_reward_total += value
return value
def _is_relevant_code_action(action: CyberSecurityOWASPAction) -> bool:
args = action.arguments or {}
text = f"{args.get('path', '')} {args.get('query', '')}".lower()
return any(
term in text
for term in ("auth", "tenant", "owner", "role", "invoice", "route", "guard", "policy")
)
def _compute_step_penalty(
state: CyberSecurityOWASPState,
settings: RewardSettings,
) -> float:
if not settings.dense_train:
return 0.0
rate = settings.value("step_penalty", 0.0)
if rate >= 0.0:
return 0.0
current = float(state.metrics.get("step_penalty_total", 0.0))
cap = settings.cap("step_penalty", -0.6)
delta = max(rate, float(cap) - current) if cap is not None else rate
state.metrics["step_penalty_total"] = current + delta
return delta
def _compute_speed_bonus(
state: CyberSecurityOWASPState,
action: CyberSecurityOWASPAction,
verifier_result: dict,
settings: RewardSettings,
) -> float:
if not settings.dense_train or action.tool_name != "submit_fix":
return 0.0
success = all(
bool((verifier_result.get(key) or {}).get("passed", False))
for key in ("security", "oracle_matrix", "regression", "public_routes", "patch_quality")
)
if not success:
return 0.0
max_steps = max(1, int(state.max_steps or 1))
bonus = settings.value("speed_bonus", 1.0) * (1.0 - min(state.step_count, max_steps) / max_steps)
return max(0.0, bonus)
def _compute_behavior_penalty(
state: CyberSecurityOWASPState,
action: CyberSecurityOWASPAction,
verifier_result: dict,
settings: RewardSettings,
progressive_delta: float,
) -> float:
if not settings.dense_train:
return 0.0
penalty = 0.0
tools = [item.get("tool_name") for item in state.action_history]
if action.tool_name == "noop":
penalty += settings.value("noop_action", -0.02)
if action.tool_name == "read_file":
path = str((action.arguments or {}).get("path", ""))
reads = [
item
for item in state.action_history
if item.get("tool_name") == "read_file"
and str((item.get("arguments") or {}).get("path", "")) == path
]
if len(reads) > 1:
penalty += settings.value("repeated_file_read", -0.05)
if action.tool_name == "send_local_request":
args = action.arguments or {}
current = (
str(args.get("method", "GET")).upper(),
str(args.get("path", "")),
str(args.get("user_id", "")),
)
matches = [
item
for item in state.action_history
if item.get("tool_name") == "send_local_request"
and (
str((item.get("arguments") or {}).get("method", "GET")).upper(),
str((item.get("arguments") or {}).get("path", "")),
str((item.get("arguments") or {}).get("user_id", "")),
)
== current
]
if len(matches) > 1:
penalty += settings.value("repeated_local_request", -0.05)
if action.tool_name == "run_visible_tests" and state.visible_test_count > 1:
penalty += settings.value("repeated_visible_tests", -0.1)
if action.tool_name == "patch_file" and not state.progress_flags.get("policy_seen"):
penalty += settings.value("patch_before_policy", -0.3)
if action.tool_name == "submit_fix":
if "patch_file" not in tools:
penalty += settings.value("submit_without_patch", -0.5)
if state.patch_attempt_count > 0 and state.visible_test_count == 0:
penalty += settings.value("submit_without_visible_tests", -0.3)
if action.tool_name == "patch_file" and state.patch_attempt_count > 3:
penalty += settings.value("excessive_patch_attempt", -0.2)
files_touched = state.metrics.get("files_touched", [])
if isinstance(files_touched, list) and len(files_touched) > 5:
penalty += settings.value("too_many_files_changed", -0.5)
if action.tool_name == "patch_file":
penalty += _oversized_patch_penalty(state, settings)
if (
progressive_delta <= 0.0
and not verifier_result.get("invalid_action")
and action.tool_name
in {
"inspect_policy_graph",
"list_routes",
"read_openapi",
"noop",
"run_visible_tests",
"send_local_request",
"compare_identities",
}
):
penalty += settings.value("no_progress_action", -0.05)
return penalty
def _oversized_patch_penalty(
state: CyberSecurityOWASPState,
settings: RewardSettings,
) -> float:
diff_lines = [
line
for line in str(state.patch_diff or "").splitlines()
if (line.startswith("+") or line.startswith("-"))
and not line.startswith("+++")
and not line.startswith("---")
]
entry = settings.entry("oversized_patch")
threshold = int(entry.get("threshold_lines", 80))
severe_threshold = int(entry.get("severe_threshold_lines", 180))
if len(diff_lines) >= severe_threshold:
return float(entry.get("severe_value", -1.0))
if len(diff_lines) >= threshold:
return settings.value("oversized_patch", -0.25)
return 0.0
def _component_total(reward: dict[str, float]) -> float:
excluded = {
"total",
"terminal_total",
"progressive",
"step_penalty",
"speed_bonus",
"token_penalty",
"behavior_penalty",
"train_total",
}
return sum(value for key, value in reward.items() if key not in excluded)
def _cap_terminal(total: float, settings: RewardSettings) -> float:
cap = settings.value("terminal_cap", 15.0)
return min(cap, total) if total > 0 else total
def _cap_train(
total: float,
settings: RewardSettings,
state: CyberSecurityOWASPState,
) -> float:
floor = settings.value("penalty_floor", -6.0)
capped = max(floor, total)
cap = settings.value("train_cap", 21.0)
if capped > 0.0:
remaining = max(0.0, cap - float(state.accumulated_reward or 0.0))
return min(capped, remaining)
return capped
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