"""
Demyst Red Team Benchmark.

Generates 50 diverse adversarial examples across 10 categories to stress-test
Demyst's detection capabilities. Goal: 100% detection rate on critical scientific errors.

Categories (5 bugs each = 50 total):
1. Mirage - Variance-destroying operations on heavy-tailed distributions
2. Leakage - Data leakage patterns in ML pipelines
3. Units - Dimensional inconsistency errors
4. Hypothesis - Statistical testing errors (p-hacking, multiple comparisons)
5. Tensor - Deep learning integrity issues
6. Reproducibility - Non-deterministic computation patterns
7. Numerical - Floating-point and numerical stability issues
8. API Misuse - Incorrect library usage patterns
9. Logic - Off-by-one and boundary condition errors
10. Statistical - Sampling and inference fallacies
"""

import logging
import os
import sys
import tempfile
from typing import Any, Dict, List, Tuple

from demyst.integrations.ci_enforcer import CIEnforcer

# Setup logging
logging.basicConfig(level=logging.INFO, format="%(message)s")
logger = logging.getLogger("demyst.red_team")


class RedTeamBenchmark:
    """Generates and runs 50 diverse adversarial test cases."""

    def __init__(self) -> None:
        self.enforcer = CIEnforcer()
        self.test_cases: List[Tuple[str, str, str]] = []  # (category, name, code)

    def generate_dataset(self) -> None:
        """Generate 50 adversarial examples across 10 categories."""
        self._generate_mirage_bugs()
        self._generate_leakage_bugs()
        self._generate_unit_bugs()
        self._generate_hypothesis_bugs()
        self._generate_tensor_bugs()
        self._generate_reproducibility_bugs()
        self._generate_numerical_bugs()
        self._generate_api_misuse_bugs()
        self._generate_logic_bugs()
        self._generate_statistical_bugs()

    def _generate_mirage_bugs(self) -> None:
        """Category 1: Variance-destroying operations (5 bugs)."""

        # 1.1: Mean on exponential distribution
        self.test_cases.append(
            (
                "Mirage",
                "mean_exponential",
                """
import numpy as np
# Exponential: heavy-tailed, mean destroys variance info
data = np.random.exponential(scale=2.0, size=10000)
result = np.mean(data)  # MIRAGE: variance information lost
""",
            )
        )

        # 1.2: Sum on pareto distribution
        self.test_cases.append(
            (
                "Mirage",
                "sum_pareto",
                """
import numpy as np
# Pareto: extreme heavy-tail, may have infinite variance
data = np.random.pareto(a=1.5, size=5000)
total = np.sum(data)  # MIRAGE: sum dominated by outliers
""",
            )
        )

        # 1.3: Argmax on lognormal
        self.test_cases.append(
            (
                "Mirage",
                "argmax_lognormal",
                """
import numpy as np
# Lognormal: skewed, argmax ignores distribution shape
scores = np.random.lognormal(mean=0, sigma=1.0, size=1000)
best_idx = np.argmax(scores)  # MIRAGE: ignores score distribution
""",
            )
        )

        # 1.4: Mean on cauchy (infinite variance)
        self.test_cases.append(
            (
                "Mirage",
                "mean_cauchy",
                """
import numpy as np
# Cauchy: no defined mean or variance
samples = np.random.standard_cauchy(size=10000)
center = np.mean(samples)  # MIRAGE: meaningless for Cauchy
""",
            )
        )

        # 1.5: Std on weibull with shape < 1
        self.test_cases.append(
            (
                "Mirage",
                "std_weibull",
                """
import numpy as np
# Weibull with k<1: highly skewed failure times
lifetimes = np.random.weibull(a=0.5, size=5000) * 100
avg_lifetime = np.mean(lifetimes)  # MIRAGE: skewed distribution
""",
            )
        )

    def _generate_leakage_bugs(self) -> None:
        """Category 2: Data leakage patterns (5 bugs)."""

        # 2.1: fit_transform before train/test split
        self.test_cases.append(
            (
                "Leakage",
                "fit_before_split",
                """
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
import numpy as np

X = np.random.randn(1000, 20)
y = np.random.randint(0, 2, 1000)

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)  # LEAKAGE: fit on all data
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y)
""",
            )
        )

        # 2.2: Target in feature set
        self.test_cases.append(
            (
                "Leakage",
                "target_in_features",
                """
import pandas as pd
from sklearn.model_selection import train_test_split

df = pd.DataFrame({
    'feature1': range(100),
    'feature2': range(100, 200),
    'target': [0, 1] * 50
})

# LEAKAGE: target accidentally left in features
X = df[['feature1', 'feature2', 'target']]
y = df['target']
X_train, X_test, y_train, y_test = train_test_split(X, y)
""",
            )
        )

        # 2.3: Future data in time series
        self.test_cases.append(
            (
                "Leakage",
                "future_data",
                """
import numpy as np
import pandas as pd

# Time series data
dates = pd.date_range('2020-01-01', periods=365, freq='D')
values = np.cumsum(np.random.randn(365))
df = pd.DataFrame({'date': dates, 'value': values})

# LEAKAGE: using future rolling mean
df['rolling_mean'] = df['value'].rolling(window=7, center=True).mean()
""",
            )
        )

        # 2.4: Test data used in validation
        self.test_cases.append(
            (
                "Leakage",
                "test_in_validation",
                """
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.ensemble import RandomForestClassifier
import numpy as np

X = np.random.randn(500, 10)
y = np.random.randint(0, 2, 500)

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# LEAKAGE: cross-validation on combined train+test
clf = RandomForestClassifier()
scores = cross_val_score(clf, X, y, cv=5)  # Uses all data including test
""",
            )
        )

        # 2.5: Feature selection on full dataset
        self.test_cases.append(
            (
                "Leakage",
                "feature_selection_leak",
                """
from sklearn.feature_selection import SelectKBest, f_classif
from sklearn.model_selection import train_test_split
import numpy as np

X = np.random.randn(1000, 50)
y = np.random.randint(0, 2, 1000)

# LEAKAGE: feature selection before split
selector = SelectKBest(f_classif, k=10)
X_selected = selector.fit_transform(X, y)  # Sees all labels
X_train, X_test, y_train, y_test = train_test_split(X_selected, y)
""",
            )
        )

    def _generate_unit_bugs(self) -> None:
        """Category 3: Dimensional inconsistency (5 bugs)."""

        # 3.1: Mass + Time
        self.test_cases.append(
            (
                "Units",
                "mass_plus_time",
                """
mass_kg = 75.0  # kilograms
time_s = 3600   # seconds
result = mass_kg + time_s  # UNITS: incompatible dimensions
""",
            )
        )

        # 3.2: Length + Velocity
        self.test_cases.append(
            (
                "Units",
                "length_plus_velocity",
                """
distance_m = 1000.0  # meters
speed_mps = 25.0     # meters per second
total = distance_m + speed_mps  # UNITS: m + m/s is invalid
""",
            )
        )

        # 3.3: Energy + Power
        self.test_cases.append(
            (
                "Units",
                "energy_plus_power",
                """
energy_joules = 5000.0  # joules (J)
power_watts = 100.0     # watts (W = J/s)
combined = energy_joules + power_watts  # UNITS: J + W is invalid
""",
            )
        )

        # 3.4: Temperature + Entropy
        self.test_cases.append(
            (
                "Units",
                "temp_plus_entropy",
                """
temperature_K = 300.0     # kelvin
entropy_JperK = 150.0     # J/K
result = temperature_K + entropy_JperK  # UNITS: K + J/K invalid
""",
            )
        )

        # 3.5: Force + Pressure
        self.test_cases.append(
            (
                "Units",
                "force_plus_pressure",
                """
force_N = 500.0       # newtons
pressure_Pa = 101325  # pascals (N/m^2)
total = force_N + pressure_Pa  # UNITS: N + Pa invalid
""",
            )
        )

    def _generate_hypothesis_bugs(self) -> None:
        """Category 4: Statistical testing errors (5 bugs)."""

        # 4.1: Multiple comparisons without correction
        self.test_cases.append(
            (
                "Hypothesis",
                "multiple_comparisons",
                """
from scipy import stats
import numpy as np

# Testing 20 hypotheses at alpha=0.05
# Expected false positives: 1 (20 * 0.05)
results = []
for i in range(20):
    a = np.random.randn(100)
    b = np.random.randn(100)
    _, p = stats.ttest_ind(a, b)
    if p < 0.05:  # HYPOTHESIS: no Bonferroni correction
        results.append(i)
""",
            )
        )

        # 4.2: P-hacking via optional stopping
        self.test_cases.append(
            (
                "Hypothesis",
                "optional_stopping",
                """
from scipy import stats
import numpy as np

# Keep collecting data until p < 0.05
np.random.seed(42)
a, b = [], []
for i in range(1000):
    a.append(np.random.randn())
    b.append(np.random.randn() + 0.1)
    if len(a) >= 20:
        _, p = stats.ttest_ind(a, b)
        if p < 0.05:  # HYPOTHESIS: p-hacking via optional stopping
            break
""",
            )
        )

        # 4.3: Underpowered test
        self.test_cases.append(
            (
                "Hypothesis",
                "underpowered_test",
                """
from scipy import stats
import numpy as np

# Effect size d=0.2 (small), need n~400 for 80% power
# Using only n=20 per group
a = np.random.randn(20)
b = np.random.randn(20) + 0.2  # Small effect
_, p = stats.ttest_ind(a, b)  # HYPOTHESIS: severely underpowered
""",
            )
        )

        # 4.4: Cherry-picking significant results
        self.test_cases.append(
            (
                "Hypothesis",
                "cherry_picking",
                """
from scipy import stats
import numpy as np

# Run many tests, report only significant ones
significant_results = []
for metric in ['accuracy', 'f1', 'precision', 'recall', 'auc']:
    baseline = np.random.randn(30)
    treatment = np.random.randn(30) + np.random.randn() * 0.3
    _, p = stats.ttest_ind(baseline, treatment)
    if p < 0.05:  # HYPOTHESIS: cherry-picking
        significant_results.append((metric, p))
# Only reporting significant_results
""",
            )
        )

        # 4.5: HARKing (Hypothesizing After Results Known)
        self.test_cases.append(
            (
                "Hypothesis",
                "harking",
                """
from scipy import stats
import numpy as np

data = np.random.randn(100, 5)  # 5 variables
target = np.random.randn(100)

# Find which variable correlates, then "hypothesize" it
correlations = [stats.pearsonr(data[:, i], target)[1] for i in range(5)]
best_var = np.argmin(correlations)  # HYPOTHESIS: HARKing
# "We hypothesized that variable {best_var} would correlate..."
""",
            )
        )

    def _generate_tensor_bugs(self) -> None:
        """Category 5: Deep learning integrity (5 bugs)."""

        # 5.1: Shape mismatch in concatenation
        self.test_cases.append(
            (
                "Tensor",
                "shape_mismatch",
                """
import numpy as np

# Attempting to concatenate mismatched shapes
tensor_a = np.random.randn(32, 128)   # batch=32, features=128
tensor_b = np.random.randn(64, 128)   # batch=64, features=128
combined = np.concatenate([tensor_a, tensor_b], axis=1)  # TENSOR: wrong axis
""",
            )
        )

        # 5.2: Dtype coercion losing precision
        self.test_cases.append(
            (
                "Tensor",
                "dtype_coercion",
                """
import numpy as np

# Gradients in float64, weights in float32
gradients = np.random.randn(1000, 1000).astype(np.float64)
weights = np.random.randn(1000, 1000).astype(np.float32)
# TENSOR: silent precision loss
updated = weights - 0.01 * gradients.astype(np.float32)
""",
            )
        )

        # 5.3: Gradient through non-differentiable op
        self.test_cases.append(
            (
                "Tensor",
                "non_differentiable",
                """
import numpy as np

# Argmax in forward pass breaks gradient flow
scores = np.random.randn(32, 10)
predictions = np.argmax(scores, axis=1)  # TENSOR: non-differentiable
# Backprop through argmax gives zero gradients everywhere
""",
            )
        )

        # 5.4: In-place modification of input
        self.test_cases.append(
            (
                "Tensor",
                "inplace_modification",
                """
import numpy as np

def normalize_inplace(x):
    # TENSOR: modifies input in-place, breaks autograd
    x -= np.mean(x)
    x /= np.std(x)
    return x

data = np.random.randn(100, 50)
normalized = normalize_inplace(data)  # Original data corrupted
""",
            )
        )

        # 5.5: Broadcasting dimension mismatch
        self.test_cases.append(
            (
                "Tensor",
                "broadcast_error",
                """
import numpy as np

# Shapes that don't broadcast correctly
batch_logits = np.random.randn(32, 10, 5)  # (batch, seq, vocab)
class_weights = np.random.randn(10)         # (num_classes,)
# TENSOR: incompatible broadcast
weighted = batch_logits * class_weights
""",
            )
        )

    def _generate_reproducibility_bugs(self) -> None:
        """Category 6: Non-deterministic computation (5 bugs)."""

        # 6.1: Unseeded random
        self.test_cases.append(
            (
                "Reproducibility",
                "unseeded_random",
                """
import numpy as np

# REPRODUCIBILITY: no seed set
train_indices = np.random.permutation(1000)[:800]
test_indices = np.random.permutation(1000)[800:]
""",
            )
        )

        # 6.2: Non-deterministic operations
        self.test_cases.append(
            (
                "Reproducibility",
                "nondeterministic_ops",
                """
import numpy as np

# Set operations are non-deterministic in order
data = [3, 1, 4, 1, 5, 9, 2, 6]
unique_vals = list(set(data))  # REPRODUCIBILITY: set order varies
""",
            )
        )

        # 6.3: Floating-point order dependency
        self.test_cases.append(
            (
                "Reproducibility",
                "float_order",
                """
import numpy as np

# Sum order affects floating-point result
values = np.random.randn(1000000).astype(np.float32)
sum1 = np.sum(values)
sum2 = np.sum(values[::-1])  # REPRODUCIBILITY: sum1 != sum2 due to float
""",
            )
        )

        # 6.4: Parallel race condition
        self.test_cases.append(
            (
                "Reproducibility",
                "parallel_race",
                """
import numpy as np
from concurrent.futures import ThreadPoolExecutor

results = []
def append_result(i):
    # REPRODUCIBILITY: race condition on shared list
    results.append(np.random.randn())

with ThreadPoolExecutor(max_workers=4) as executor:
    executor.map(append_result, range(100))
""",
            )
        )

        # 6.5: Cache/memoization dependency
        self.test_cases.append(
            (
                "Reproducibility",
                "cache_dependency",
                """
import functools
import numpy as np

@functools.lru_cache(maxsize=100)
def compute_feature(x):
    # REPRODUCIBILITY: cache state affects results across runs
    return x * np.random.randn()

features = [compute_feature(i) for i in range(50)]
""",
            )
        )

    def _generate_numerical_bugs(self) -> None:
        """Category 7: Floating-point and stability (5 bugs)."""

        # 7.1: Catastrophic cancellation
        self.test_cases.append(
            (
                "Numerical",
                "catastrophic_cancellation",
                """
import numpy as np

# Subtracting nearly equal numbers
a = 1.0000000001
b = 1.0000000000
diff = a - b  # NUMERICAL: catastrophic cancellation
relative_error = abs((diff - 1e-10) / 1e-10)
""",
            )
        )

        # 7.2: Overflow in intermediate computation
        self.test_cases.append(
            (
                "Numerical",
                "overflow_intermediate",
                """
import numpy as np

# Softmax without numerical stability
logits = np.array([1000.0, 1001.0, 1002.0])
exp_logits = np.exp(logits)  # NUMERICAL: overflow to inf
softmax = exp_logits / np.sum(exp_logits)  # nan
""",
            )
        )

        # 7.3: Underflow to zero
        self.test_cases.append(
            (
                "Numerical",
                "underflow_zero",
                """
import numpy as np

# Product of small probabilities
log_probs = np.random.uniform(-10, -5, size=100)
probs = np.exp(log_probs)
joint_prob = np.prod(probs)  # NUMERICAL: underflows to 0
""",
            )
        )

        # 7.4: Denormalized numbers
        self.test_cases.append(
            (
                "Numerical",
                "denormalized",
                """
import numpy as np

# Operations near floating-point minimum
tiny = np.finfo(np.float64).tiny
x = tiny / 1000  # NUMERICAL: denormalized, slow operations
result = x * x / x
""",
            )
        )

        # 7.5: Precision loss in accumulation
        self.test_cases.append(
            (
                "Numerical",
                "precision_loss",
                """
import numpy as np

# Adding small numbers to large accumulator
total = 1e16
for _ in range(1000000):
    total += 1.0  # NUMERICAL: 1.0 lost due to precision
# total is still ~1e16, not 1e16 + 1e6
""",
            )
        )

    def _generate_api_misuse_bugs(self) -> None:
        """Category 8: Incorrect library usage (5 bugs)."""

        # 8.1: Wrong argument order
        self.test_cases.append(
            (
                "API",
                "wrong_arg_order",
                """
import numpy as np

# np.linspace(start, stop, num) - common mistake
x = np.linspace(0, 100, 10)  # Correct
y = np.linspace(100, 0, 10)  # API: probably meant num=100, stop=10
""",
            )
        )

        # 8.2: Silent failure mode
        self.test_cases.append(
            (
                "API",
                "silent_failure",
                """
import numpy as np

# Integer division when float expected
a = np.array([1, 2, 3, 4, 5])
mean = sum(a) / len(a)  # API: use np.mean, this works but fragile
""",
            )
        )

        # 8.3: Type coercion surprise
        self.test_cases.append(
            (
                "API",
                "type_coercion",
                """
import numpy as np

# Unexpected integer array
data = np.array([1, 2, 3])
data[0] = 1.5  # API: silently truncated to 1
""",
            )
        )

        # 8.4: Implicit broadcast
        self.test_cases.append(
            (
                "API",
                "implicit_broadcast",
                """
import numpy as np

# Accidental broadcast
a = np.random.randn(10, 1)
b = np.random.randn(10)
c = a + b  # API: broadcasts to (10, 10), probably wanted (10,)
""",
            )
        )

        # 8.5: Deprecated function
        self.test_cases.append(
            (
                "API",
                "deprecated_function",
                """
import numpy as np

# Using deprecated API
matrix = np.matrix([[1, 2], [3, 4]])  # API: np.matrix is deprecated
result = matrix * matrix.T
""",
            )
        )

    def _generate_logic_bugs(self) -> None:
        """Category 9: Off-by-one and boundary (5 bugs)."""

        # 9.1: Off-by-one in loop
        self.test_cases.append(
            (
                "Logic",
                "off_by_one_loop",
                """
import numpy as np

data = np.random.randn(100)
# LOGIC: should be range(len(data) - 1) for pairwise
for i in range(len(data)):
    diff = data[i+1] - data[i]  # IndexError on last iteration
""",
            )
        )

        # 9.2: Fence-post error
        self.test_cases.append(
            (
                "Logic",
                "fence_post",
                """
import numpy as np

# Count intervals vs points
start, end, step = 0, 10, 1
num_points = (end - start) / step  # LOGIC: should be +1 for fence-post
points = np.linspace(start, end, int(num_points))
""",
            )
        )

        # 9.3: Boundary condition
        self.test_cases.append(
            (
                "Logic",
                "boundary_condition",
                """
import numpy as np

def find_threshold(arr, thresh):
    # LOGIC: doesn't handle empty array
    return arr[arr > thresh].min()

data = np.random.randn(100)
result = find_threshold(data, 100)  # Fails if no values > 100
""",
            )
        )

        # 9.4: Empty collection
        self.test_cases.append(
            (
                "Logic",
                "empty_collection",
                """
import numpy as np

def compute_stats(arr):
    # LOGIC: doesn't check for empty
    return {
        'mean': np.mean(arr),
        'std': np.std(arr),
        'min': np.min(arr)  # RuntimeWarning on empty
    }

result = compute_stats(np.array([]))
""",
            )
        )

        # 9.5: Null propagation
        self.test_cases.append(
            (
                "Logic",
                "null_propagation",
                """
import numpy as np

data = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
# LOGIC: NaN propagates silently
mean = np.mean(data)  # Returns nan
total = np.sum(data)  # Returns nan
""",
            )
        )

    def _generate_statistical_bugs(self) -> None:
        """Category 10: Sampling and inference fallacies (5 bugs)."""

        # 10.1: Simpson's paradox
        self.test_cases.append(
            (
                "Statistical",
                "simpsons_paradox",
                """
import numpy as np

# Aggregate trend reverses in subgroups
# STATISTICAL: ignoring confounding variable
group_a_success = 80 / 100  # 80%
group_b_success = 90 / 200  # 45%
overall_success = (80 + 90) / (100 + 200)  # 56.7%
# But within subgroups, B might actually be better
""",
            )
        )

        # 10.2: Selection bias
        self.test_cases.append(
            (
                "Statistical",
                "selection_bias",
                """
import numpy as np

# Only analyzing "successful" cases
all_attempts = np.random.randn(1000)
# STATISTICAL: selection bias - only keeping positives
successful = all_attempts[all_attempts > 0]
avg_success = np.mean(successful)  # Biased estimate
""",
            )
        )

        # 10.3: Survivorship bias
        self.test_cases.append(
            (
                "Statistical",
                "survivorship_bias",
                """
import numpy as np

# Analyzing only companies that survived
# STATISTICAL: survivorship bias
surviving_company_returns = np.random.randn(100) + 0.5  # Positive bias
market_return_estimate = np.mean(surviving_company_returns)
# Ignores all the failed companies with negative returns
""",
            )
        )

        # 10.4: Regression to mean
        self.test_cases.append(
            (
                "Statistical",
                "regression_to_mean",
                """
import numpy as np

# Extreme performers selected for follow-up
test_scores = np.random.randn(1000)
top_performers = np.argsort(test_scores)[-100:]  # Top 10%
# STATISTICAL: regression to mean - their retest will be lower
retest_scores = np.random.randn(100)  # Independent of first test
""",
            )
        )

        # 10.5: Ecological fallacy
        self.test_cases.append(
            (
                "Statistical",
                "ecological_fallacy",
                """
import numpy as np

# Group-level stats applied to individuals
# STATISTICAL: ecological fallacy
state_avg_income = np.random.uniform(40000, 80000, 50)
state_avg_education = np.random.uniform(12, 18, 50)
# Correlation at state level doesn't imply individual correlation
group_correlation = np.corrcoef(state_avg_income, state_avg_education)[0, 1]
""",
            )
        )

    def run_attack(self) -> bool:
        """Run the benchmark suite."""
        if not self.test_cases:
            self.generate_dataset()

        logger.info(
            f"Starting Red Team Benchmark with {len(self.test_cases)} adversarial scenarios..."
        )
        logger.info(f"Categories: 10 | Tests per category: 5")
        logger.info("-" * 60)

        detected_count = 0
        total_count = len(self.test_cases)
        category_results: Dict[str, Dict[str, int]] = {}

        for category, name, code in self.test_cases:
            if category not in category_results:
                category_results[category] = {"detected": 0, "total": 0}
            category_results[category]["total"] += 1

            # Create temp file
            with tempfile.NamedTemporaryFile(mode="w", suffix=".py", delete=False) as f:
                f.write(code)
                temp_path = f.name

            try:
                result = self.enforcer.analyze_file(temp_path)
                detected = self._check_detection(category, result)

                if detected:
                    detected_count += 1
                    category_results[category]["detected"] += 1
                else:
                    logger.error(f"MISSED: [{category}] {name}")
                    logger.error(f"  Code snippet: {code[:100].strip()}...")

            finally:
                os.unlink(temp_path)

        # Print summary
        logger.info("\n" + "=" * 60)
        logger.info("RED TEAM BENCHMARK RESULTS")
        logger.info("=" * 60)

        for category, stats in sorted(category_results.items()):
            pct = (stats["detected"] / stats["total"]) * 100 if stats["total"] > 0 else 0
            status = "PASS" if stats["detected"] == stats["total"] else "FAIL"
            logger.info(
                f"  {category:20} {stats['detected']}/{stats['total']} ({pct:5.1f}%) [{status}]"
            )

        logger.info("-" * 60)
        success_rate = (detected_count / total_count) * 100
        logger.info(f"  TOTAL: {detected_count}/{total_count} detected ({success_rate:.1f}%)")

        if detected_count == total_count:
            logger.info("\n  DEFENSE SUCCESSFUL: All 50 adversarial attacks repelled.")
            return True
        else:
            logger.error(f"\n  DEFENSE FAILED: {total_count - detected_count} attacks penetrated.")
            return False

    def _check_detection(self, category: str, result: Dict[str, Any]) -> bool:
        """Check if the bug was detected based on category."""

        if category == "Mirage":
            return bool(result.get("mirage", {}).get("issues"))

        elif category == "Leakage":
            return bool(result.get("leakage", {}).get("violations"))

        elif category == "Units":
            violations = result.get("unit", {}).get("violations", [])
            return any(v.get("type") == "incompatible_addition" for v in violations)

        elif category == "Hypothesis":
            # Check for hypothesis guard findings
            hyp = result.get("hypothesis", {})
            return bool(hyp.get("issues") or hyp.get("violations"))

        elif category == "Tensor":
            tensor = result.get("tensor", {})
            return bool(tensor.get("issues") or tensor.get("violations"))

        elif category in ("Reproducibility", "Numerical", "API", "Logic", "Statistical"):
            # These may be detected by various guards
            for guard in ["mirage", "leakage", "hypothesis", "tensor", "unit"]:
                guard_result = result.get(guard, {})
                if guard_result.get("issues") or guard_result.get("violations"):
                    return True
            return False

        return False

    def generate_attack(self, target_guard: str, strategy: str = "adversarial") -> Dict[str, Any]:
        """
        Generate a red-teaming attack against a specific guard.

        Args:
            target_guard: Name of the guard to attack
            strategy: Attack strategy

        Returns:
            Attack results
        """
        return {}  # Placeholder implementation


# Backwards compatibility alias
RedTeamAttacker = RedTeamBenchmark


if __name__ == "__main__":
    benchmark = RedTeamBenchmark()
    benchmark.generate_dataset()
    success = benchmark.run_attack()
    sys.exit(0 if success else 1)
