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3aeaf3d | 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 | from __future__ import annotations
import time
from typing import Any
from .actions import ActionParseError, parse_action
from .curriculum import CurriculumManager
from .direction_library import DirectionLibrary
from .executor import EnvironmentExecutor
from .observations import build_blue_observation, build_red_observation
from .constants import MAX_TURNS, STEP_TIMEOUT_SECS
from .rewards import compute_blue_reward, compute_red_reward
from .secrets_bank import RulesBank, SecretsBank
from .state import BlueAction, BlueResources, RedAction, TargetSystemState
from .target_system import build_target_system
try:
from openenv import Environment
_BASE = Environment
except ImportError:
_BASE = object # graceful fallback for local dev
class SeigeEnv(_BASE):
def __init__(self) -> None:
self.direction_library = DirectionLibrary()
self.target = build_target_system(self.direction_library)
self.executor = EnvironmentExecutor(self.target)
self.curriculum = CurriculumManager()
self.secrets_bank = SecretsBank()
self.rules_bank = RulesBank()
self._state: TargetSystemState | None = None
self._episode_red_rewards: list[float] = []
self._episode_blue_rewards: list[float] = []
def reset(self) -> dict:
config = self.curriculum.get_config()
self._state = TargetSystemState.sample(
secrets_bank=self.secrets_bank,
rules_bank=self.rules_bank,
baseline=self.target.baseline_means,
num_sessions=config["num_sessions"],
)
self._state.stage = self.curriculum.stage
self._state.blue_resources = BlueResources(
probe_budget=config["probe_budget"],
log_sample_rate=config["log_sample_rate"],
latency_ticks=config["latency_ticks"],
)
self._episode_red_rewards = []
self._episode_blue_rewards = []
return {
"red": build_red_observation(self._state, config, self.target.get_num_layers()).to_dict(),
"blue": build_blue_observation(self._state, config).to_dict(),
}
def step(self, action: dict[str, Any] | str) -> dict:
if self._state is None:
raise RuntimeError("Call reset() before step()")
start = time.time()
try:
parsed = parse_action(action)
except ActionParseError as exc:
return self._error_result(str(exc))
if time.time() - start > STEP_TIMEOUT_SECS:
return self._timeout_result()
if isinstance(parsed, RedAction):
result = self.executor.execute_red(parsed, self._state)
reward = compute_red_reward(result, self._state, result.strategy_embedding)
self._episode_red_rewards.append(reward)
observation = build_red_observation(
self._state,
self.curriculum.get_config(),
self.target.get_num_layers(),
).to_dict()
elif isinstance(parsed, BlueAction):
result = self.executor.execute_blue(parsed, self._state)
reward = compute_blue_reward(result, self._state)
self._episode_blue_rewards.append(reward)
observation = build_blue_observation(self._state, self.curriculum.get_config()).to_dict()
else:
return self._error_result("Unknown parsed action")
self._state.tick += 1
self._state.blue_resources.probes_used = 0
done = (
result.episode_terminal
or self._state.tick >= MAX_TURNS
or any(session.extraction_succeeded for session in self._state.sessions.values())
)
if done:
avg_red = sum(self._episode_red_rewards) / max(1, len(self._episode_red_rewards))
avg_blue = sum(self._episode_blue_rewards) / max(1, len(self._episode_blue_rewards))
self.curriculum.record(avg_red, avg_blue)
if self.curriculum.should_advance():
self.curriculum.advance()
info_dict = result.info_dict()
reward_info = {
"reward/total": reward,
"reward/extraction": 10.0 * float(result.extraction_succeeded),
"reward/policy_bypass": 5.0 * float(result.policy_bypassed),
"reward/evasion": 4.0 * float(result.success and not result.detected),
"reward/detected_penalty": -3.0 * float(result.detected),
}
info_dict.update(reward_info)
try:
import wandb
if wandb.run:
wandb.log(reward_info, step=self._state.tick)
except ImportError:
pass
return {
"observation": observation,
"reward": reward,
"done": done,
"info": info_dict,
}
def state(self) -> dict:
if self._state is None:
return {}
return {
"tick": self._state.tick,
"stage": self._state.stage,
"num_sessions": len(self._state.sessions),
"active_attacks": sum(1 for session in self._state.sessions.values() if session.attack_active),
"detections": sum(1 for session in self._state.sessions.values() if session.detected),
}
def _error_result(self, message: str) -> dict:
return {"observation": {}, "reward": -1.0, "done": False, "info": {"error": message}}
def _timeout_result(self) -> dict:
return {"observation": {}, "reward": -2.0, "done": True, "info": {"error": "timeout"}}
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