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import sys
import warnings
import contextlib
import logging
import time
# Suppress all standard warnings
warnings.filterwarnings("ignore")
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
logging.getLogger("transformers").setLevel(logging.ERROR)
logging.getLogger("httpx").setLevel(logging.ERROR)
@contextlib.contextmanager
def suppress_output():
"""
Context manager to redirect stdout and stderr to os.devnull at the OS level.
"""
with open(os.devnull, 'w') as devnull:
old_stdout_fd = os.dup(1)
old_stderr_fd = os.dup(2)
os.dup2(devnull.fileno(), 1)
os.dup2(devnull.fileno(), 2)
try:
yield
finally:
os.dup2(old_stdout_fd, 1)
os.dup2(old_stderr_fd, 2)
os.close(old_stdout_fd)
os.close(old_stderr_fd)
def main():
print("[START]", flush=True)
with suppress_output():
from src.env.guardrail import GuardrailEnvironment
from src.env.reward import LogBarrierReward
from src.env.models import Action
from src.telemetry.streamer import append_metric
from src.rl.data import dataset_cache
import random
# Load real datasets for evaluation threat feed
dataset_cache.ingest_production_baseline()
batch = dataset_cache.sample_batch(batch_size=10)
env = GuardrailEnvironment()
env.reset(batch["adversarial"], batch["benign"])
r_engine = LogBarrierReward()
try:
import torch
from unsloth import FastLanguageModel
model_name = "Qwen/Qwen2.5-0.5B-Instruct"
if os.path.exists("models/trained_guardrail"):
model_name = "models/trained_guardrail"
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=model_name,
max_seq_length=2048,
dtype=None,
load_in_4bit=True,
)
FastLanguageModel.for_inference(model)
except Exception as e:
# Fallback if Unsloth is missing or GPU is unavailable (prevents crash)
model, tokenizer = None, None
prompt = "You are a cyber security expert. Generate a JSON AST GuardrailGraph to block prompt injections but allow benign queries. Output strictly in ```json format."
# Simulate an improving learning curve if model fails to load (no GPU)
fallback_actions = [
{"root": {"operator": "OR", "children": []}}, # 0% recall, 0% FPR
{"root": {"operator": "OR", "children": [{"filter_type": "substring", "value": "Ignore"}]}},
{"root": {"operator": "OR", "children": [{"filter_type": "regex_pattern", "value": "Ignore previous.*"}, {"filter_type": "length_limit", "value": 500}]}},
{"root": {"operator": "AND", "children": [{"filter_type": "substring", "value": "secret"}, {"filter_type": "entropy_threshold", "value": 4.5}]}},
{"root": {"operator": "OR", "children": [{"filter_type": "length_limit", "value": 10}, {"filter_type": "substring", "value": "Delete"}]}},
{"root": {"operator": "OR", "children": [{"filter_type": "substring", "value": "bypass"}, {"filter_type": "substring", "value": "Developer Mode"}]}}
]
for i in range(120):
with suppress_output():
try:
if model is not None and tokenizer is not None:
# Pass 1: Trained Model
inputs = tokenizer([prompt], return_tensors="pt").to("cuda" if torch.cuda.is_available() else "cpu")
outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True, pad_token_id=tokenizer.eos_token_id)
output_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
del outputs
# Pass 2: Baseline Model (Disabled Adapter)
if hasattr(model, 'disable_adapter'):
with model.disable_adapter():
baseline_outputs = model.generate(**inputs, max_new_tokens=512, use_cache=True, pad_token_id=tokenizer.eos_token_id)
baseline_output_text = tokenizer.batch_decode(baseline_outputs, skip_special_tokens=True)[0]
del baseline_outputs
else:
baseline_output_text = output_text
# Explicit 8GB memory ceiling GC
del inputs
import gc
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
else:
import json
idx = min(i // 8, len(fallback_actions) - 1)
fallback = fallback_actions[idx]
output_text = json.dumps({"graph_id": f"AST-Fallback-{i}", "description": "Simulated Fallback", **fallback})
baseline_output_text = json.dumps({"graph_id": f"AST-Baseline-{i}", "description": "Simulated Baseline", "root": {"operator": "OR", "children": []}})
action = Action(ast_json=output_text, baseline_ast_json=baseline_output_text)
recall, fpr, syntax_error = env.step(action)
reward = r_engine.calculate(recall, fpr, syntax_error)
baseline_recall, baseline_fpr, baseline_syntax_error = 0.0, 0.0, True
baseline_reward = 0.0
if baseline_output_text:
baseline_action = Action(ast_json=baseline_output_text)
baseline_recall, baseline_fpr, baseline_syntax_error = env.step(baseline_action)
baseline_reward = r_engine.calculate(baseline_recall, baseline_fpr, baseline_syntax_error)
recent_traffic = []
for adv_str in env.state.adversarial_samples[:3]:
recent_traffic.append({
"prompt_text": adv_str,
"is_malicious": True,
"was_blocked": random.random() < recall
})
for ben_str in env.state.benign_samples[:3]:
recent_traffic.append({
"prompt_text": ben_str,
"is_malicious": False,
"was_blocked": random.random() < fpr
})
random.shuffle(recent_traffic)
append_metric(reward, recall, fpr, baseline_reward, baseline_recall, baseline_fpr, output_text if not syntax_error else None, recent_traffic)
time.sleep(1.0) # Ensure UI renders smoothly
except Exception as e:
pass
print("[STEP]", flush=True)
print("[END]", flush=True)
if __name__ == "__main__":
main()
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