Senior Security

Senior Security Engineer

The agent performs STRIDE threat analysis with DREAD risk scoring, designs defense-in-depth security architectures with Zero Trust principles, conducts secure code reviews against OWASP Top 10, and scans codebases for hardcoded secrets across 20+ credential patterns.

Core Capabilities

• Threat modeling — STRIDE per-element analysis, DREAD risk scoring, DFD creation, attack trees, and mitigation mapping.
• Security architecture — defense-in-depth layering, Zero Trust, authentication pattern selection (OAuth/OIDC, JWT, mTLS, FIDO2), and encryption strategy.
• Vulnerability assessment — automated (SAST/DAST/dependency/secret) plus manual testing, OWASP Top 10 mapping, severity classification, and remediation tracking.
• Secure code review — auth/authz, data handling, and crypto review with a checklist and secure-vs-insecure pattern catalog.
• Incident response — triage, containment, eradication, recovery, post-mortem, with severity tiers and runbook checklist.
• Secret detection — secret_scanner.py finds 20+ credential patterns (AWS/GCP/Azure, GitHub/Slack/Stripe, private keys); CI/CD-ready exit codes.
• Compliance mapping — OWASP ASVS, CIS Benchmarks, NIST CSF, PCI-DSS, HIPAA, SOC 2 at the application layer.

When to Use

• Conducting a threat model or attack-surface analysis on a system or component.
• Reviewing code for vulnerabilities before deployment.
• Designing a secure, defense-in-depth or Zero Trust architecture.
• Scanning a codebase for hardcoded secrets and credentials.
• Running a vulnerability assessment or planning incident response.

Clarify First

Before the threat model or scan, confirm these inputs. If any is unknown or vague, ASK — do not assume:

• [ ] Target component/system — what to threat-model or scan (--component; defines the STRIDE analysis scope)
• [ ] Assets & trust boundaries — what is being protected and where untrusted input enters (drives DREAD scoring and mitigations)
• [ ] Task — threat model / code vuln review / secret scan / incident-response plan (selects the tool and the output)

Stop rule: ask only the 2-3 that most change the output. If the user says "just draft it," proceed and list your assumptions at the top of the artifact.

Tools

References

Load the reference that matches the task — keep this file lean and pull detail on demand:

• [references/security-workflows.md](references/security-workflows.md) — the 5 full workflows (threat modeling, security architecture, vulnerability assessment, secure code review, incident response) with all decision matrices (STRIDE, OWASP Top 10, severity, code-review checklist), the security tools catalog, compliance frameworks, security headers, anti-patterns, troubleshooting table, and success criteria. Read when executing any security workflow.
• [references/threat-modeling-guide.md](references/threat-modeling-guide.md) — STRIDE methodology, attack trees, DREAD scoring, DFD creation, threat templates. Read when building a threat model.
• [references/security-architecture-patterns.md](references/security-architecture-patterns.md) — Zero Trust, defense-in-depth, authentication patterns (OAuth/PKCE, JWT, MFA), API security, data protection, secret management. Read when designing secure architecture.
• [references/cryptography-implementation.md](references/cryptography-implementation.md) — AES-GCM, ChaCha20, RSA, Ed25519, password hashing (Argon2/bcrypt), HMAC, key management/rotation, HSM integration, common crypto mistakes. Read when implementing or reviewing cryptography.
• [references/tool-reference.md](references/tool-reference.md) — full flag tables, usage examples, and output formats for threat_modeler.py and secret_scanner.py. Read when running the bundled scripts.

Scope & Limitations

This skill covers:

• Application-level security: threat modeling, secure code review, secret detection, and vulnerability assessment for web applications and APIs.
• Security architecture design: defense-in-depth layering, Zero Trust patterns, authentication/authorization model selection, and encryption strategy.
• Incident response planning: severity classification, containment procedures, post-mortem frameworks, and runbook creation.
• Compliance mapping: OWASP ASVS, CIS Benchmarks, NIST CSF, PCI-DSS, HIPAA, and SOC 2 alignment at the application layer.

This skill does NOT cover:

• Infrastructure and cloud security hardening (see senior-devops and aws-solution-architect).
• Runtime security monitoring, SIEM rule authoring, and SOC operations (see senior-secops).
• Full regulatory compliance programs, audit evidence collection, and certification processes (see ra-qm-team).
• Network penetration testing tooling, red team operations, and physical security assessments.

Integration Points

Cryptography Implementation Guide

Practical cryptographic patterns for securing data at rest, in transit, and in use.

---

Table of Contents

• Cryptographic Primitives
• Symmetric Encryption
• Asymmetric Encryption
• Hashing and Password Storage
• Key Management
• Common Cryptographic Mistakes

---

Cryptographic Primitives

Algorithm Selection Guide

Security Strength Comparison

---

Symmetric Encryption

AES-256-GCM Implementation

from cryptography.hazmat.primitives.ciphers.aead import AESGCM
import os

class AESGCMEncryption:
    """
    AES-256-GCM authenticated encryption.

    Provides both confidentiality and integrity.
    GCM mode prevents tampering with authentication tag.
    """

    def __init__(self, key: bytes = None):
        if key is None:
            key = AESGCM.generate_key(bit_length=256)
        if len(key) != 32:
            raise ValueError("Key must be 32 bytes (256 bits)")
        self.key = key
        self.aesgcm = AESGCM(key)

    def encrypt(self, plaintext: bytes, associated_data: bytes = None) -> bytes:
        """
        Encrypt with random nonce.

        Returns: nonce (12 bytes) + ciphertext + tag (16 bytes)
        """
        nonce = os.urandom(12)  # 96-bit nonce for GCM
        ciphertext = self.aesgcm.encrypt(nonce, plaintext, associated_data)
        return nonce + ciphertext

    def decrypt(self, ciphertext: bytes, associated_data: bytes = None) -> bytes:
        """
        Decrypt and verify authentication tag.

        Raises InvalidTag if tampered.
        """
        nonce = ciphertext[:12]
        actual_ciphertext = ciphertext[12:]
        return self.aesgcm.decrypt(nonce, actual_ciphertext, associated_data)


# Usage
encryptor = AESGCMEncryption()
plaintext = b"Sensitive data to encrypt"
aad = b"user_id:12345"  # Authenticated but not encrypted

ciphertext = encryptor.encrypt(plaintext, associated_data=aad)
decrypted = encryptor.decrypt(ciphertext, associated_data=aad)

ChaCha20-Poly1305 Implementation

from cryptography.hazmat.primitives.ciphers.aead import ChaCha20Poly1305
import os

class ChaChaEncryption:
    """
    ChaCha20-Poly1305 authenticated encryption.

    Faster than AES on systems without hardware AES support.
    Resistant to timing attacks (constant-time implementation).
    """

    def __init__(self, key: bytes = None):
        if key is None:
            key = ChaCha20Poly1305.generate_key()
        self.key = key
        self.chacha = ChaCha20Poly1305(key)

    def encrypt(self, plaintext: bytes, associated_data: bytes = None) -> bytes:
        """Encrypt with random 96-bit nonce."""
        nonce = os.urandom(12)
        ciphertext = self.chacha.encrypt(nonce, plaintext, associated_data)
        return nonce + ciphertext

    def decrypt(self, ciphertext: bytes, associated_data: bytes = None) -> bytes:
        """Decrypt and verify Poly1305 authentication tag."""
        nonce = ciphertext[:12]
        actual_ciphertext = ciphertext[12:]
        return self.chacha.decrypt(nonce, actual_ciphertext, associated_data)

Envelope Encryption Pattern

"""
Envelope Encryption: Encrypt data with a Data Encryption Key (DEK),
then encrypt DEK with a Key Encryption Key (KEK).

Benefits:
- KEK can be rotated without re-encrypting data
- DEK can be stored alongside encrypted data
- Enables per-record encryption with different DEKs
"""

from cryptography.hazmat.primitives.ciphers.aead import AESGCM
from cryptography.hazmat.primitives import serialization
from cryptography.hazmat.primitives.asymmetric import padding
from cryptography.hazmat.primitives import hashes
import os
import json
import base64

class EnvelopeEncryption:
    def __init__(self, kek_public_key, kek_private_key=None):
        self.kek_public = kek_public_key
        self.kek_private = kek_private_key

    def encrypt(self, plaintext: bytes) -> dict:
        """
        1. Generate random DEK
        2. Encrypt plaintext with DEK
        3. Encrypt DEK with KEK
        4. Return encrypted DEK + encrypted data
        """
        # Generate Data Encryption Key
        dek = AESGCM.generate_key(bit_length=256)
        aesgcm = AESGCM(dek)

        # Encrypt data with DEK
        nonce = os.urandom(12)
        encrypted_data = aesgcm.encrypt(nonce, plaintext, None)

        # Encrypt DEK with KEK (RSA-OAEP)
        encrypted_dek = self.kek_public.encrypt(
            dek,
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA256()),
                algorithm=hashes.SHA256(),
                label=None
            )
        )

        return {
            'encrypted_dek': base64.b64encode(encrypted_dek).decode(),
            'nonce': base64.b64encode(nonce).decode(),
            'ciphertext': base64.b64encode(encrypted_data).decode()
        }

    def decrypt(self, envelope: dict) -> bytes:
        """
        1. Decrypt DEK with KEK
        2. Decrypt data with DEK
        """
        if self.kek_private is None:
            raise ValueError("Private key required for decryption")

        # Decrypt DEK
        encrypted_dek = base64.b64decode(envelope['encrypted_dek'])
        dek = self.kek_private.decrypt(
            encrypted_dek,
            padding.OAEP(
                mgf=padding.MGF1(algorithm=hashes.SHA256()),
                algorithm=hashes.SHA256(),
                label=None
            )
        )

        # Decrypt data
        aesgcm = AESGCM(dek)
        nonce = base64.b64decode(envelope['nonce'])
        ciphertext = base64.b64decode(envelope['ciphertext'])

        return aesgcm.decrypt(nonce, ciphertext, None)

---

Asymmetric Encryption

RSA Key Generation and Usage

from cryptography.hazmat.primitives.asymmetric import rsa, padding
from cryptography.hazmat.primitives import hashes, serialization

def generate_rsa_keypair(key_size=4096):
    """Generate RSA key pair for encryption/signing."""
    private_key = rsa.generate_private_key(
        public_exponent=65537,
        key_size=key_size
    )
    public_key = private_key.public_key()

    return private_key, public_key

def serialize_keys(private_key, public_key, password=None):
    """Serialize keys for storage."""
    # Private key (encrypted with password)
    if password:
        encryption = serialization.BestAvailableEncryption(password.encode())
    else:
        encryption = serialization.NoEncryption()

    private_pem = private_key.private_bytes(
        encoding=serialization.Encoding.PEM,
        format=serialization.PrivateFormat.PKCS8,
        encryption_algorithm=encryption
    )

    # Public key
    public_pem = public_key.public_bytes(
        encoding=serialization.Encoding.PEM,
        format=serialization.PublicFormat.SubjectPublicKeyInfo
    )

    return private_pem, public_pem

def rsa_encrypt(public_key, plaintext: bytes) -> bytes:
    """RSA-OAEP encryption (for small data like keys)."""
    return public_key.encrypt(
        plaintext,
        padding.OAEP(
            mgf=padding.MGF1(algorithm=hashes.SHA256()),
            algorithm=hashes.SHA256(),
            label=None
        )
    )

def rsa_decrypt(private_key, ciphertext: bytes) -> bytes:
    """RSA-OAEP decryption."""
    return private_key.decrypt(
        ciphertext,
        padding.OAEP(
            mgf=padding.MGF1(algorithm=hashes.SHA256()),
            algorithm=hashes.SHA256(),
            label=None
        )
    )

Digital Signatures (Ed25519)

from cryptography.hazmat.primitives.asymmetric.ed25519 import (
    Ed25519PrivateKey, Ed25519PublicKey
)

class Ed25519Signer:
    """
    Ed25519 digital signatures.

    Fast, secure, and deterministic.
    256-bit keys provide 128-bit security.
    """

    def __init__(self, private_key=None):
        if private_key is None:
            private_key = Ed25519PrivateKey.generate()
        self.private_key = private_key
        self.public_key = private_key.public_key()

    def sign(self, message: bytes) -> bytes:
        """Create digital signature."""
        return self.private_key.sign(message)

    def verify(self, message: bytes, signature: bytes) -> bool:
        """Verify digital signature."""
        try:
            self.public_key.verify(signature, message)
            return True
        except Exception:
            return False

    def get_public_key_bytes(self) -> bytes:
        """Export public key for verification."""
        return self.public_key.public_bytes(
            encoding=serialization.Encoding.Raw,
            format=serialization.PublicFormat.Raw
        )


# Usage for message signing
signer = Ed25519Signer()
message = b"Important document content"
signature = signer.sign(message)

# Verification (can be done with public key only)
is_valid = signer.verify(message, signature)

ECDH Key Exchange

from cryptography.hazmat.primitives.asymmetric import x25519
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
from cryptography.hazmat.primitives import hashes

class X25519KeyExchange:
    """
    X25519 Diffie-Hellman key exchange.

    Used to establish shared secrets over insecure channels.
    """

    def __init__(self):
        self.private_key = x25519.X25519PrivateKey.generate()
        self.public_key = self.private_key.public_key()

    def get_public_key_bytes(self) -> bytes:
        """Get public key to send to peer."""
        return self.public_key.public_bytes(
            encoding=serialization.Encoding.Raw,
            format=serialization.PublicFormat.Raw
        )

    def derive_shared_key(self, peer_public_key_bytes: bytes,
                          info: bytes = b"") -> bytes:
        """
        Derive shared encryption key from peer's public key.

        Uses HKDF to derive a proper encryption key.
        """
        peer_public_key = x25519.X25519PublicKey.from_public_bytes(
            peer_public_key_bytes
        )

        shared_secret = self.private_key.exchange(peer_public_key)

        # Derive encryption key using HKDF
        derived_key = HKDF(
            algorithm=hashes.SHA256(),
            length=32,
            salt=None,
            info=info,
        ).derive(shared_secret)

        return derived_key


# Key exchange example
alice = X25519KeyExchange()
bob = X25519KeyExchange()

# Exchange public keys (can be done over insecure channel)
alice_public = alice.get_public_key_bytes()
bob_public = bob.get_public_key_bytes()

# Both derive the same shared key
alice_shared = alice.derive_shared_key(bob_public, info=b"session-key")
bob_shared = bob.derive_shared_key(alice_public, info=b"session-key")

assert alice_shared == bob_shared  # Same key!

---

Hashing and Password Storage

Password Hashing with Argon2

import argon2
from argon2 import PasswordHasher, Type

class SecurePasswordHasher:
    """
    Argon2id password hashing.

    Argon2id combines resistance to:
    - GPU attacks (memory-hard)
    - Side-channel attacks (data-independent)
    """

    def __init__(self):
        # OWASP recommended parameters
        self.hasher = PasswordHasher(
            time_cost=3,        # Iterations
            memory_cost=65536,  # 64 MB
            parallelism=4,      # Threads
            hash_len=32,        # Output length
            type=Type.ID        # Argon2id variant
        )

    def hash_password(self, password: str) -> str:
        """
        Hash password for storage.

        Returns encoded string with algorithm parameters and salt.
        """
        return self.hasher.hash(password)

    def verify_password(self, password: str, hash: str) -> bool:
        """
        Verify password against stored hash.

        Automatically handles timing-safe comparison.
        """
        try:
            self.hasher.verify(hash, password)
            return True
        except argon2.exceptions.VerifyMismatchError:
            return False

    def needs_rehash(self, hash: str) -> bool:
        """Check if hash needs upgrading to current parameters."""
        return self.hasher.check_needs_rehash(hash)


# Usage
hasher = SecurePasswordHasher()

# During registration
password = "user_password_123!"
password_hash = hasher.hash_password(password)
# Store password_hash in database

# During login
stored_hash = password_hash  # From database
if hasher.verify_password("user_password_123!", stored_hash):
    print("Login successful")

    # Check if hash needs upgrading
    if hasher.needs_rehash(stored_hash):
        new_hash = hasher.hash_password(password)
        # Update stored hash

Bcrypt Alternative

import bcrypt

class BcryptHasher:
    """
    Bcrypt password hashing.

    Well-established, widely supported.
    Use when Argon2 is not available.
    """

    def __init__(self, rounds=12):
        self.rounds = rounds

    def hash_password(self, password: str) -> str:
        salt = bcrypt.gensalt(rounds=self.rounds)
        return bcrypt.hashpw(password.encode(), salt).decode()

    def verify_password(self, password: str, hash: str) -> bool:
        return bcrypt.checkpw(password.encode(), hash.encode())

HMAC for Message Authentication

import hmac
import hashlib
import secrets

def create_hmac(key: bytes, message: bytes) -> bytes:
    """Create HMAC-SHA256 authentication tag."""
    return hmac.new(key, message, hashlib.sha256).digest()

def verify_hmac(key: bytes, message: bytes, tag: bytes) -> bool:
    """Verify HMAC in constant time."""
    expected = hmac.new(key, message, hashlib.sha256).digest()
    return hmac.compare_digest(expected, tag)

# API request signing example
def sign_api_request(secret_key: bytes, method: str, path: str,
                     body: bytes, timestamp: str) -> str:
    """Sign API request for authentication."""
    message = f"{method}\n{path}\n{timestamp}\n".encode() + body
    signature = create_hmac(secret_key, message)
    return signature.hex()

---

Key Management

Key Derivation Functions

from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
from cryptography.hazmat.primitives.kdf.scrypt import Scrypt
from cryptography.hazmat.primitives import hashes
import os

def derive_key_pbkdf2(password: str, salt: bytes = None,
                       iterations: int = 600000) -> tuple:
    """
    Derive encryption key from password using PBKDF2.

    NIST recommends minimum 600,000 iterations for PBKDF2-SHA256.
    """
    if salt is None:
        salt = os.urandom(16)

    kdf = PBKDF2HMAC(
        algorithm=hashes.SHA256(),
        length=32,
        salt=salt,
        iterations=iterations
    )

    key = kdf.derive(password.encode())
    return key, salt

def derive_key_scrypt(password: str, salt: bytes = None) -> tuple:
    """
    Derive key using scrypt (memory-hard).

    More resistant to hardware attacks than PBKDF2.
    """
    if salt is None:
        salt = os.urandom(16)

    kdf = Scrypt(
        salt=salt,
        length=32,
        n=2**17,  # CPU/memory cost
        r=8,      # Block size
        p=1       # Parallelization
    )

    key = kdf.derive(password.encode())
    return key, salt

Key Rotation Strategy

from datetime import datetime, timedelta
from typing import Dict, Optional
import json

class KeyManager:
    """
    Manage encryption key lifecycle.

    Supports key rotation without data re-encryption.
    """

    def __init__(self, storage_backend):
        self.storage = storage_backend

    def generate_key(self, key_id: str, algorithm: str = 'AES-256-GCM') -> dict:
        """Generate and store new encryption key."""
        key_material = os.urandom(32)

        key_metadata = {
            'key_id': key_id,
            'algorithm': algorithm,
            'created_at': datetime.utcnow().isoformat(),
            'expires_at': (datetime.utcnow() + timedelta(days=365)).isoformat(),
            'status': 'active'
        }

        self.storage.store_key(key_id, key_material, key_metadata)
        return key_metadata

    def rotate_key(self, old_key_id: str) -> dict:
        """
        Rotate encryption key.

        1. Mark old key as 'decrypt-only'
        2. Generate new key as 'active'
        3. Old key can still decrypt, new key encrypts
        """
        # Mark old key as decrypt-only
        old_metadata = self.storage.get_key_metadata(old_key_id)
        old_metadata['status'] = 'decrypt-only'
        self.storage.update_key_metadata(old_key_id, old_metadata)

        # Generate new key
        new_key_id = f"{old_key_id.rsplit('_', 1)[0]}_{datetime.utcnow().strftime('%Y%m%d')}"
        return self.generate_key(new_key_id)

    def get_encryption_key(self) -> tuple:
        """Get current active key for encryption."""
        return self.storage.get_active_key()

    def get_decryption_key(self, key_id: str) -> bytes:
        """Get specific key for decryption."""
        return self.storage.get_key(key_id)

Hardware Security Module Integration

# AWS CloudHSM / KMS integration pattern
import boto3

class AWSKMSProvider:
    """
    AWS KMS integration for key management.

    Keys never leave AWS infrastructure.
    """

    def __init__(self, key_id: str, region: str = 'us-east-1'):
        self.kms = boto3.client('kms', region_name=region)
        self.key_id = key_id

    def encrypt(self, plaintext: bytes) -> bytes:
        """Encrypt using KMS master key."""
        response = self.kms.encrypt(
            KeyId=self.key_id,
            Plaintext=plaintext
        )
        return response['CiphertextBlob']

    def decrypt(self, ciphertext: bytes) -> bytes:
        """Decrypt using KMS master key."""
        response = self.kms.decrypt(
            KeyId=self.key_id,
            CiphertextBlob=ciphertext
        )
        return response['Plaintext']

    def generate_data_key(self) -> tuple:
        """Generate data encryption key."""
        response = self.kms.generate_data_key(
            KeyId=self.key_id,
            KeySpec='AES_256'
        )
        return response['Plaintext'], response['CiphertextBlob']

---

Common Cryptographic Mistakes

Mistake 1: Using ECB Mode

# BAD: ECB mode reveals patterns
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes

def bad_ecb_encrypt(key, plaintext):
    cipher = Cipher(algorithms.AES(key), modes.ECB())
    encryptor = cipher.encryptor()
    return encryptor.update(plaintext) + encryptor.finalize()

# GOOD: Use authenticated encryption (GCM)
from cryptography.hazmat.primitives.ciphers.aead import AESGCM

def good_gcm_encrypt(key, plaintext):
    aesgcm = AESGCM(key)
    nonce = os.urandom(12)
    return nonce + aesgcm.encrypt(nonce, plaintext, None)

Mistake 2: Reusing Nonces

# BAD: Static nonce
nonce = b"fixed_nonce!"  # NEVER DO THIS

# GOOD: Random nonce per encryption
nonce = os.urandom(12)

# ALSO GOOD: Counter-based nonce (if you can guarantee no repeats)
class NonceCounter:
    def __init__(self):
        self.counter = 0

    def get_nonce(self):
        self.counter += 1
        return self.counter.to_bytes(12, 'big')

Mistake 3: Rolling Your Own Crypto

# BAD: Custom "encryption"
def bad_encrypt(data, key):
    return bytes([b ^ k for b, k in zip(data, key * len(data))])

# GOOD: Use established libraries
from cryptography.fernet import Fernet

def good_encrypt(data, key):
    f = Fernet(key)
    return f.encrypt(data)

Mistake 4: Weak Random Generation

import random
import secrets

# BAD: Predictable random
def bad_generate_token():
    return ''.join(random.choices('abcdef0123456789', k=32))

# GOOD: Cryptographically secure
def good_generate_token():
    return secrets.token_hex(16)

Mistake 5: Timing Attacks in Comparison

# BAD: Early exit reveals length
def bad_compare(a, b):
    if len(a) != len(b):
        return False
    for x, y in zip(a, b):
        if x != y:
            return False
    return True

# GOOD: Constant-time comparison
import hmac

def good_compare(a, b):
    return hmac.compare_digest(a, b)

---

Quick Reference Card

Security Architecture Patterns

Proven security architecture patterns for designing resilient systems.

---

Table of Contents

• Zero Trust Architecture
• Defense in Depth
• Secure Authentication Patterns
• API Security Patterns
• Data Protection Patterns
• Security Anti-Patterns

---

Zero Trust Architecture

Never trust, always verify. Every request authenticated and authorized regardless of network location.

Core Principles

Implementation Components

ZERO TRUST ARCHITECTURE

┌─────────────────────────────────────────────────────────────┐
│                        CONTROL PLANE                         │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐      │
│  │   Identity   │  │    Policy    │  │   Threat     │      │
│  │   Provider   │  │    Engine    │  │  Intelligence│      │
│  └──────────────┘  └──────────────┘  └──────────────┘      │
└─────────────────────────────────────────────────────────────┘
                              │
                    ┌─────────┴─────────┐
                    │   Policy Decision │
                    │   Point (PDP)     │
                    └─────────┬─────────┘
                              │
┌─────────────────────────────┴───────────────────────────────┐
│                        DATA PLANE                            │
│  ┌──────────────┐                      ┌──────────────┐     │
│  │    User      │──── PEP ────────────▶│   Resource   │     │
│  │   Device     │      │               │   (App/Data) │     │
│  └──────────────┘      │               └──────────────┘     │
│                   Policy Enforcement                         │
│                   Point (PEP)                               │
└─────────────────────────────────────────────────────────────┘

Authentication Flow

# Zero Trust authentication middleware
import jwt
from functools import wraps

def zero_trust_auth(required_claims=None):
    """
    Verify every request against identity, device, and context.
    """
    def decorator(f):
        @wraps(f)
        def decorated(*args, **kwargs):
            token = request.headers.get('Authorization', '').replace('Bearer ', '')

            # 1. Verify token signature and expiration
            try:
                payload = jwt.decode(token, PUBLIC_KEY, algorithms=['RS256'])
            except jwt.InvalidTokenError:
                return {'error': 'Invalid token'}, 401

            # 2. Verify device compliance
            device_id = request.headers.get('X-Device-ID')
            if not verify_device_compliance(device_id, payload['user_id']):
                return {'error': 'Device not compliant'}, 403

            # 3. Verify location/network context
            client_ip = request.remote_addr
            if not verify_network_context(client_ip, payload['allowed_networks']):
                return {'error': 'Network context invalid'}, 403

            # 4. Verify required claims
            if required_claims:
                for claim in required_claims:
                    if claim not in payload:
                        return {'error': f'Missing claim: {claim}'}, 403

            # 5. Log access for analytics
            log_access_attempt(payload, request, 'allowed')

            return f(*args, **kwargs)
        return decorated
    return decorator

@app.route('/api/sensitive-data')
@zero_trust_auth(required_claims=['data:read', 'clearance:secret'])
def get_sensitive_data():
    return fetch_data()

Network Segmentation

---

Defense in Depth

Multiple layers of security controls so failure of one doesn't compromise the system.

Security Layers

DEFENSE IN DEPTH LAYERS

┌─────────────────────────────────────────────────────────────┐
│  Layer 1: PERIMETER                                          │
│  - Firewall, WAF, DDoS mitigation, DNS filtering            │
├─────────────────────────────────────────────────────────────┤
│  Layer 2: NETWORK                                            │
│  - Segmentation, IDS/IPS, network monitoring, VPN           │
├─────────────────────────────────────────────────────────────┤
│  Layer 3: HOST                                               │
│  - Endpoint protection, hardening, patching, logging        │
├─────────────────────────────────────────────────────────────┤
│  Layer 4: APPLICATION                                        │
│  - Input validation, authentication, secure coding, SAST    │
├─────────────────────────────────────────────────────────────┤
│  Layer 5: DATA                                               │
│  - Encryption at rest/transit, access controls, DLP, backup │
└─────────────────────────────────────────────────────────────┘

Implementation Checklist

Fail-Safe Defaults

# Secure default configuration
class SecurityConfig:
    # Authentication
    SESSION_COOKIE_SECURE = True
    SESSION_COOKIE_HTTPONLY = True
    SESSION_COOKIE_SAMESITE = 'Strict'

    # Headers
    CONTENT_SECURITY_POLICY = "default-src 'self'; script-src 'self'"
    X_FRAME_OPTIONS = 'DENY'
    X_CONTENT_TYPE_OPTIONS = 'nosniff'
    REFERRER_POLICY = 'strict-origin-when-cross-origin'

    # Timeouts
    SESSION_LIFETIME = 3600  # 1 hour
    TOKEN_EXPIRY = 900  # 15 minutes

    # Rate limiting
    RATE_LIMIT_DEFAULT = '100/hour'
    RATE_LIMIT_AUTH = '10/minute'

---

Secure Authentication Patterns

OAuth 2.0 + PKCE Flow

OAUTH 2.0 AUTHORIZATION CODE FLOW WITH PKCE

┌──────────┐                                  ┌──────────────┐
│  Client  │                                  │    Auth      │
│  (SPA)   │                                  │   Server     │
└────┬─────┘                                  └──────┬───────┘
     │                                               │
     │ 1. Generate code_verifier (random string)     │
     │    code_challenge = SHA256(code_verifier)     │
     │                                               │
     │ 2. /authorize?                                │
     │    response_type=code&                        │
     │    client_id=xxx&                             │
     │    code_challenge=xxx&                        │
     │    code_challenge_method=S256                 │
     │──────────────────────────────────────────────▶│
     │                                               │
     │◀──────────────────────────────────────────────│
     │ 3. Redirect with authorization_code           │
     │                                               │
     │ 4. POST /token                                │
     │    grant_type=authorization_code&             │
     │    code=xxx&                                  │
     │    code_verifier=xxx  (proves possession)     │
     │──────────────────────────────────────────────▶│
     │                                               │
     │◀──────────────────────────────────────────────│
     │ 5. { access_token, refresh_token, id_token }  │
     │                                               │

JWT Token Structure

# Secure JWT implementation
import jwt
import secrets
from datetime import datetime, timedelta

class JWTService:
    def __init__(self, private_key, public_key, issuer):
        self.private_key = private_key
        self.public_key = public_key
        self.issuer = issuer

    def create_access_token(self, user_id, roles, expires_minutes=15):
        """Create short-lived access token."""
        now = datetime.utcnow()
        payload = {
            'iss': self.issuer,
            'sub': str(user_id),
            'iat': now,
            'exp': now + timedelta(minutes=expires_minutes),
            'jti': secrets.token_hex(16),  # Unique token ID
            'roles': roles,
            'type': 'access'
        }
        return jwt.encode(payload, self.private_key, algorithm='RS256')

    def create_refresh_token(self, user_id, expires_days=7):
        """Create longer-lived refresh token (stored server-side)."""
        now = datetime.utcnow()
        jti = secrets.token_hex(32)
        payload = {
            'iss': self.issuer,
            'sub': str(user_id),
            'iat': now,
            'exp': now + timedelta(days=expires_days),
            'jti': jti,
            'type': 'refresh'
        }
        # Store jti in database for revocation capability
        store_refresh_token(jti, user_id, now + timedelta(days=expires_days))
        return jwt.encode(payload, self.private_key, algorithm='RS256')

    def verify_token(self, token, token_type='access'):
        """Verify token with all security checks."""
        try:
            payload = jwt.decode(
                token,
                self.public_key,
                algorithms=['RS256'],
                issuer=self.issuer
            )

            # Verify token type
            if payload.get('type') != token_type:
                raise jwt.InvalidTokenError('Invalid token type')

            # For refresh tokens, check revocation
            if token_type == 'refresh':
                if is_token_revoked(payload['jti']):
                    raise jwt.InvalidTokenError('Token revoked')

            return payload

        except jwt.ExpiredSignatureError:
            raise AuthError('Token expired')
        except jwt.InvalidTokenError as e:
            raise AuthError(f'Invalid token: {e}')

Multi-Factor Authentication

# TOTP implementation
import pyotp
import qrcode

class TOTPService:
    def __init__(self, issuer_name):
        self.issuer = issuer_name

    def generate_secret(self):
        """Generate a new TOTP secret for user."""
        return pyotp.random_base32()

    def get_provisioning_uri(self, secret, user_email):
        """Generate QR code URI for authenticator app."""
        totp = pyotp.TOTP(secret)
        return totp.provisioning_uri(
            name=user_email,
            issuer_name=self.issuer
        )

    def verify_code(self, secret, code, valid_window=1):
        """Verify TOTP code with time drift tolerance."""
        totp = pyotp.TOTP(secret)
        return totp.verify(code, valid_window=valid_window)

---

API Security Patterns

Input Validation

from pydantic import BaseModel, validator, constr
import re

class UserCreateRequest(BaseModel):
    """Strict input validation for user creation."""

    email: constr(max_length=255)
    username: constr(min_length=3, max_length=50, regex=r'^[a-zA-Z0-9_]+$')
    password: constr(min_length=12, max_length=128)

    @validator('email')
    def validate_email(cls, v):
        email_regex = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
        if not re.match(email_regex, v):
            raise ValueError('Invalid email format')
        return v.lower()

    @validator('password')
    def validate_password_strength(cls, v):
        if not re.search(r'[A-Z]', v):
            raise ValueError('Password must contain uppercase letter')
        if not re.search(r'[a-z]', v):
            raise ValueError('Password must contain lowercase letter')
        if not re.search(r'\d', v):
            raise ValueError('Password must contain digit')
        if not re.search(r'[!@#$%^&*(),.?":{}|<>]', v):
            raise ValueError('Password must contain special character')
        return v

Rate Limiting

from redis import Redis
from functools import wraps
import time

class RateLimiter:
    """Token bucket rate limiter with Redis backend."""

    def __init__(self, redis_client):
        self.redis = redis_client

    def is_allowed(self, key, limit, window_seconds):
        """Check if request is within rate limit."""
        pipe = self.redis.pipeline()
        now = time.time()
        window_start = now - window_seconds

        # Remove old entries
        pipe.zremrangebyscore(key, 0, window_start)
        # Count current entries
        pipe.zcard(key)
        # Add new entry
        pipe.zadd(key, {str(now): now})
        # Set expiry
        pipe.expire(key, window_seconds)

        results = pipe.execute()
        current_count = results[1]

        return current_count < limit

def rate_limit(limit=100, window=3600, key_func=None):
    """Rate limiting decorator."""
    def decorator(f):
        @wraps(f)
        def decorated(*args, **kwargs):
            if key_func:
                key = f"rate_limit:{key_func()}"
            else:
                key = f"rate_limit:{request.remote_addr}:{f.__name__}"

            if not rate_limiter.is_allowed(key, limit, window):
                return {
                    'error': 'Rate limit exceeded',
                    'retry_after': window
                }, 429

            return f(*args, **kwargs)
        return decorated
    return decorator

SQL Injection Prevention

# NEVER: String concatenation
# query = f"SELECT * FROM users WHERE id = {user_id}"

# ALWAYS: Parameterized queries
from sqlalchemy import text

def get_user_secure(user_id):
    """Safe parameterized query."""
    query = text("SELECT * FROM users WHERE id = :user_id")
    result = db.execute(query, {'user_id': user_id})
    return result.fetchone()

# For dynamic queries, use ORM
def search_users(filters):
    """Safe dynamic query with ORM."""
    query = User.query

    if 'name' in filters:
        # ORM handles escaping
        query = query.filter(User.name.ilike(f"%{filters['name']}%"))

    if 'role' in filters:
        query = query.filter(User.role == filters['role'])

    return query.all()

---

Data Protection Patterns

Encryption at Rest

from cryptography.fernet import Fernet
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import base64
import os

class FieldEncryption:
    """Encrypt sensitive database fields."""

    def __init__(self, master_key):
        self.fernet = Fernet(master_key)

    @staticmethod
    def derive_key(password, salt):
        """Derive encryption key from password."""
        kdf = PBKDF2HMAC(
            algorithm=hashes.SHA256(),
            length=32,
            salt=salt,
            iterations=480000,
        )
        key = base64.urlsafe_b64encode(kdf.derive(password.encode()))
        return key

    def encrypt(self, plaintext):
        """Encrypt a field value."""
        if isinstance(plaintext, str):
            plaintext = plaintext.encode()
        return self.fernet.encrypt(plaintext).decode()

    def decrypt(self, ciphertext):
        """Decrypt a field value."""
        if isinstance(ciphertext, str):
            ciphertext = ciphertext.encode()
        return self.fernet.decrypt(ciphertext).decode()

# Usage in ORM
class User(db.Model):
    id = db.Column(db.Integer, primary_key=True)
    email = db.Column(db.String(255))  # Not sensitive
    _ssn = db.Column('ssn', db.String(500))  # Encrypted

    @property
    def ssn(self):
        if self._ssn:
            return field_encryption.decrypt(self._ssn)
        return None

    @ssn.setter
    def ssn(self, value):
        if value:
            self._ssn = field_encryption.encrypt(value)
        else:
            self._ssn = None

Secret Management

# HashiCorp Vault integration
import hvac

class VaultClient:
    def __init__(self, url, token):
        self.client = hvac.Client(url=url, token=token)

    def get_secret(self, path):
        """Retrieve secret from Vault."""
        secret = self.client.secrets.kv.v2.read_secret_version(path=path)
        return secret['data']['data']

    def get_database_credentials(self, role):
        """Get dynamic database credentials."""
        creds = self.client.secrets.database.generate_credentials(role)
        return {
            'username': creds['data']['username'],
            'password': creds['data']['password'],
            'ttl': creds['lease_duration']
        }

---

Security Anti-Patterns

Anti-Pattern: Security Through Obscurity

Anti-Pattern: Trusting Client Input

# BAD: Trusting client-provided data
@app.route('/admin')
def admin_panel():
    # Client can forge this header!
    if request.headers.get('X-Is-Admin') == 'true':
        return render_admin()

# GOOD: Server-side verification
@app.route('/admin')
@login_required
def admin_panel():
    if not current_user.has_role('admin'):
        abort(403)
    return render_admin()

Anti-Pattern: Hardcoded Secrets

# BAD: Hardcoded credentials
DATABASE_URL = "postgresql://admin:SuperSecret123@localhost/db"
API_KEY = "sk-1234567890abcdef"

# GOOD: Environment variables + secret management
import os
DATABASE_URL = os.environ['DATABASE_URL']
API_KEY = vault_client.get_secret('api/keys')['api_key']

Anti-Pattern: Verbose Error Messages

# BAD: Reveals internal information
except Exception as e:
    return {'error': str(e), 'stack_trace': traceback.format_exc()}, 500

# GOOD: Generic message, detailed logging
except Exception as e:
    logger.exception(f"Internal error: {e}")
    return {'error': 'An internal error occurred', 'request_id': request_id}, 500

---

Security Tools Reference

Security Workflows & Operations

Read this when conducting a threat model, designing a secure architecture, running a vulnerability assessment, performing a secure code review, or handling a security incident — these are the full step-by-step workflows, decision matrices, tool catalogs, standards, anti-patterns, troubleshooting, and success criteria.

Threat Modeling Workflow

Identify and analyze security threats using STRIDE methodology.

Workflow: Conduct Threat Model

1. Define system scope and boundaries:

• Identify assets to protect
• Map trust boundaries
• Document data flows

2. Create data flow diagram:

• External entities (users, services)
• Processes (application components)
• Data stores (databases, caches)
• Data flows (APIs, network connections)

3. Apply STRIDE to each DFD element:

• Spoofing: Can identity be faked?
• Tampering: Can data be modified?
• Repudiation: Can actions be denied?
• Information Disclosure: Can data leak?
• Denial of Service: Can availability be affected?
• Elevation of Privilege: Can access be escalated?

4. Score risks using DREAD:

• Damage potential (1-10)
• Reproducibility (1-10)
• Exploitability (1-10)
• Affected users (1-10)
• Discoverability (1-10)

5. Prioritize threats by risk score 6. Define mitigations for each threat 7. Document in threat model report 8. Validation: All DFD elements analyzed; STRIDE applied; threats scored; mitigations mapped

STRIDE Threat Categories

STRIDE per Element Matrix

See: threat-modeling-guide.md

---

Security Architecture Workflow

Design secure systems using defense-in-depth principles.

Workflow: Design Secure Architecture

1. Define security requirements:

• Compliance requirements (GDPR, HIPAA, PCI-DSS)
• Data classification (public, internal, confidential, restricted)
• Threat model inputs

2. Apply defense-in-depth layers:

• Perimeter: WAF, DDoS protection, rate limiting
• Network: Segmentation, IDS/IPS, mTLS
• Host: Patching, EDR, hardening
• Application: Input validation, authentication, secure coding
• Data: Encryption at rest and in transit

3. Implement Zero Trust principles:

• Verify explicitly (every request)
• Least privilege access (JIT/JEA)
• Assume breach (segment, monitor)

4. Configure authentication and authorization:

• Identity provider selection
• MFA requirements
• RBAC/ABAC model

5. Design encryption strategy:

• Key management approach
• Algorithm selection
• Certificate lifecycle

6. Plan security monitoring:

• Log aggregation
• SIEM integration
• Alerting rules

7. Document architecture decisions 8. Validation: Defense-in-depth layers defined; Zero Trust applied; encryption strategy documented; monitoring planned

Defense-in-Depth Layers

Layer 1: PERIMETER
  WAF, DDoS mitigation, DNS filtering, rate limiting

Layer 2: NETWORK
  Segmentation, IDS/IPS, network monitoring, VPN, mTLS

Layer 3: HOST
  Endpoint protection, OS hardening, patching, logging

Layer 4: APPLICATION
  Input validation, authentication, secure coding, SAST

Layer 5: DATA
  Encryption at rest/transit, access controls, DLP, backup

Authentication Pattern Selection

See: security-architecture-patterns.md

---

Vulnerability Assessment Workflow

Identify and remediate security vulnerabilities in applications.

Workflow: Conduct Vulnerability Assessment

1. Define assessment scope:

• In-scope systems and applications
• Testing methodology (black box, gray box, white box)
• Rules of engagement

2. Gather information:

• Technology stack inventory
• Architecture documentation
• Previous vulnerability reports

3. Perform automated scanning:

• SAST (static analysis)
• DAST (dynamic analysis)
• Dependency scanning
• Secret detection

4. Conduct manual testing:

• Business logic flaws
• Authentication bypass
• Authorization issues
• Injection vulnerabilities

5. Classify findings by severity:

• Critical: Immediate exploitation risk
• High: Significant impact, easier to exploit
• Medium: Moderate impact or difficulty
• Low: Minor impact

6. Develop remediation plan:

• Prioritize by risk
• Assign owners
• Set deadlines

7. Verify fixes and document 8. Validation: Scope defined; automated and manual testing complete; findings classified; remediation tracked

OWASP Top 10 Mapping

Vulnerability Severity Matrix

---

Secure Code Review Workflow

Review code for security vulnerabilities before deployment.

Workflow: Conduct Security Code Review

1. Establish review scope:

• Changed files and functions
• Security-sensitive areas (auth, crypto, input handling)
• Third-party integrations

2. Run automated analysis:

• SAST tools (Semgrep, CodeQL, Bandit)
• Secret scanning
• Dependency vulnerability check

3. Review authentication code:

• Password handling (hashing, storage)
• Session management
• Token validation

4. Review authorization code:

• Access control checks
• RBAC implementation
• Privilege boundaries

5. Review data handling:

• Input validation
• Output encoding
• SQL query construction
• File path handling

6. Review cryptographic code:

• Algorithm selection
• Key management
• Random number generation

7. Document findings with severity 8. Validation: Automated scans passed; auth/authz reviewed; data handling checked; crypto verified; findings documented

Security Code Review Checklist

Secure vs Insecure Patterns

---

Incident Response Workflow

Respond to and contain security incidents.

Workflow: Handle Security Incident

1. Identify and triage:

• Validate incident is genuine
• Assess initial scope and severity
• Activate incident response team

2. Contain the threat:

• Isolate affected systems
• Block malicious IPs/accounts
• Disable compromised credentials

3. Eradicate root cause:

• Remove malware/backdoors
• Patch vulnerabilities
• Update configurations

4. Recover operations:

• Restore from clean backups
• Verify system integrity
• Monitor for recurrence

5. Conduct post-mortem:

• Timeline reconstruction
• Root cause analysis
• Lessons learned

6. Implement improvements:

• Update detection rules
• Enhance controls
• Update runbooks

7. Document and report 8. Validation: Threat contained; root cause eliminated; systems recovered; post-mortem complete; improvements implemented

Incident Severity Levels

Incident Response Checklist

---

Security Tools Reference

Recommended Security Tools

Cryptographic Algorithm Selection

See: cryptography-implementation.md

---

Security Standards Reference

Compliance Frameworks

Security Headers Checklist

---

Anti-Patterns

• Security by obscurity -- hiding endpoints or using non-standard ports is not a control; implement authentication, authorization, and encryption
• MD5/SHA1 for password hashing -- both are broken for this purpose; use Argon2id or bcrypt with cost factor >= 12
• Math.random for tokens -- predictable values allow session hijacking; use crypto.getRandomValues() or secrets.token_hex()
• Shell=True in subprocess -- enables command injection; use argument lists with subprocess.run(["cmd", "arg"])
• Threat model without data flow diagram -- STRIDE analysis requires DFD elements to be systematic; skip the DFD and you miss entire attack surfaces
• Accepted risks without review cadence -- DREAD scores drift as systems evolve; re-validate accepted risks every 90 days

---

Troubleshooting

---

Success Criteria

• Zero critical- or high-severity secrets detected by secret_scanner.py across the entire codebase before every release.
• Threat model coverage above 90%: every component in the data flow diagram has a completed STRIDE analysis with documented mitigations.
• All OWASP Top 10 vulnerability categories addressed in the security architecture with at least one compensating control per category.
• Mean time to remediate critical vulnerabilities under 48 hours from discovery to verified fix.
• 100% of authentication and authorization code paths reviewed with the Secure Code Review Checklist before merge.
• Incident response exercises (tabletop or simulated) conducted at least quarterly with post-mortem documentation.
• DREAD risk scores for all remaining accepted risks reviewed and re-validated every 90 days.

Threat Modeling Guide

Systematic approaches for identifying, analyzing, and mitigating security threats.

---

Table of Contents

• Threat Modeling Process
• STRIDE Framework
• Attack Trees
• DREAD Risk Scoring
• Data Flow Diagrams
• Common Attack Patterns

---

Threat Modeling Process

Workflow: Conduct Threat Model

1. Define the scope and objectives:

• System boundaries
• Assets to protect
• Trust levels

2. Create data flow diagram:

• External entities
• Processes
• Data stores
• Data flows
• Trust boundaries

3. Identify threats using STRIDE:

• Apply STRIDE to each DFD element
• Document threat scenarios

4. Analyze and prioritize risks:

• Score using DREAD
• Rank by severity

5. Define mitigations:

• Map controls to threats
• Identify gaps

6. Validate and iterate:

• Review with team
• Update as system evolves

7. Document in threat model report 8. Validation: All DFD elements analyzed; threats documented; mitigations mapped; residual risks accepted

Threat Model Template

THREAT MODEL REPORT

System: [System Name]
Version: [Version]
Date: [Date]
Author: [Name]

1. SYSTEM OVERVIEW
   - Purpose: [Description]
   - Users: [User types]
   - Data: [Data classification]

2. SCOPE
   - In Scope: [Components included]
   - Out of Scope: [Components excluded]
   - Assumptions: [Security assumptions]

3. DATA FLOW DIAGRAM
   [DFD image or ASCII representation]

4. THREATS IDENTIFIED
   | ID | Element | STRIDE | Threat | DREAD | Mitigation |
   |----|---------|--------|--------|-------|------------|

5. RESIDUAL RISKS
   [Accepted risks with justification]

6. RECOMMENDATIONS
   [Prioritized security improvements]

---

STRIDE Framework

Categorization model for identifying threats.

STRIDE Categories

STRIDE per Element

STRIDE Analysis Template

STRIDE ANALYSIS

Element: User Authentication Service
Type: Process

┌─────────────────────────────────────────────────────────────────┐
│ SPOOFING                                                        │
├─────────────────────────────────────────────────────────────────┤
│ Threat: Attacker uses stolen credentials to impersonate user   │
│ Attack Vector: Phishing, credential stuffing, session hijack   │
│ Likelihood: High                                                │
│ Impact: High - Full account access                              │
│ Mitigation: MFA, session binding, anomaly detection             │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│ TAMPERING                                                       │
├─────────────────────────────────────────────────────────────────┤
│ Threat: Attacker modifies authentication request in transit    │
│ Attack Vector: Man-in-the-middle, request manipulation         │
│ Likelihood: Medium                                              │
│ Impact: High - Bypass authentication                            │
│ Mitigation: TLS 1.3, request signing, HSTS                      │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│ REPUDIATION                                                     │
├─────────────────────────────────────────────────────────────────┤
│ Threat: User denies performing privileged action               │
│ Attack Vector: Claim account was compromised                   │
│ Likelihood: Medium                                              │
│ Impact: Medium - Dispute resolution difficulty                  │
│ Mitigation: Comprehensive audit logging, log integrity         │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│ INFORMATION DISCLOSURE                                          │
├─────────────────────────────────────────────────────────────────┤
│ Threat: Password hashes exposed via SQL injection              │
│ Attack Vector: SQLi, backup exposure, error messages           │
│ Likelihood: Medium                                              │
│ Impact: Critical - Mass credential compromise                   │
│ Mitigation: Parameterized queries, encryption, error handling  │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│ DENIAL OF SERVICE                                               │
├─────────────────────────────────────────────────────────────────┤
│ Threat: Brute force attacks overwhelm authentication service   │
│ Attack Vector: Credential stuffing, distributed attacks        │
│ Likelihood: High                                                │
│ Impact: High - Users cannot authenticate                        │
│ Mitigation: Rate limiting, CAPTCHA, account lockout            │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│ ELEVATION OF PRIVILEGE                                          │
├─────────────────────────────────────────────────────────────────┤
│ Threat: Regular user gains admin privileges                    │
│ Attack Vector: JWT manipulation, IDOR, role confusion          │
│ Likelihood: Medium                                              │
│ Impact: Critical - Full system compromise                       │
│ Mitigation: Server-side authorization, signed tokens, RBAC     │
└─────────────────────────────────────────────────────────────────┘

Threat Mitigation Matrix

---

Attack Trees

Visual representation of attack paths to a specific goal.

Attack Tree Structure

ATTACK TREE: Compromise User Account

                    ┌─────────────────────┐
                    │ GOAL: Access User   │
                    │      Account        │
                    └──────────┬──────────┘
                               │
           ┌───────────────────┼───────────────────┐
           │                   │                   │
    ┌──────┴──────┐     ┌──────┴──────┐     ┌──────┴──────┐
    │   Obtain    │     │   Bypass    │     │   Exploit   │
    │ Credentials │     │    Auth     │     │   Session   │
    │    [OR]     │     │    [OR]     │     │    [OR]     │
    └──────┬──────┘     └──────┬──────┘     └──────┬──────┘
           │                   │                   │
     ┌─────┼─────┐       ┌─────┼─────┐       ┌─────┼─────┐
     │     │     │       │     │     │       │     │     │
   ┌─┴─┐ ┌─┴─┐ ┌─┴─┐   ┌─┴─┐ ┌─┴─┐ ┌─┴─┐   ┌─┴─┐ ┌─┴─┐ ┌─┴─┐
   │Phi│ │Crd│ │Key│   │SQL│ │JWT│ │Pwd│   │XSS│ │Fix│ │Sid│
   │sh │ │Stf│ │Log│   │ i │ │Frg│ │Rst│   │   │ │tn │ │Hj │
   └───┘ └───┘ └───┘   └───┘ └───┘ └───┘   └───┘ └───┘ └───┘

Legend:
- Phi: Phishing
- CrdStf: Credential Stuffing
- KeyLog: Keylogger
- SQLi: SQL Injection
- JWTFrg: JWT Forgery
- PwdRst: Password Reset Flaw
- XSS: Cross-Site Scripting
- Fixtn: Session Fixation
- SidHj: Session Hijacking

Attack Tree Analysis

Calculating Attack Probability

def calculate_attack_probability(attack_tree_node):
    """
    Calculate cumulative probability of attack success.

    For OR nodes: P = 1 - (1-P1)(1-P2)...(1-Pn)
    For AND nodes: P = P1 * P2 * ... * Pn
    """
    if node.is_leaf:
        return node.probability

    child_probs = [calculate_attack_probability(c) for c in node.children]

    if node.operator == 'OR':
        # At least one path succeeds
        prob_all_fail = 1
        for p in child_probs:
            prob_all_fail *= (1 - p)
        return 1 - prob_all_fail

    elif node.operator == 'AND':
        # All paths must succeed
        prob_all_succeed = 1
        for p in child_probs:
            prob_all_succeed *= p
        return prob_all_succeed

---

DREAD Risk Scoring

Quantitative risk assessment for prioritizing threats.

DREAD Components

DREAD Scoring Guide

Damage Potential:

Reproducibility:

Exploitability:

Affected Users:

Discoverability:

DREAD Calculation

def calculate_dread_score(damage, reproducibility, exploitability,
                           affected_users, discoverability):
    """
    Calculate DREAD risk score.

    Returns: Float between 1-10
    Risk Levels:
        8-10: Critical
        6-7.9: High
        4-5.9: Medium
        1-3.9: Low
    """
    score = (damage + reproducibility + exploitability +
             affected_users + discoverability) / 5
    return round(score, 1)

def get_risk_level(dread_score):
    if dread_score >= 8:
        return 'Critical'
    elif dread_score >= 6:
        return 'High'
    elif dread_score >= 4:
        return 'Medium'
    else:
        return 'Low'

DREAD Assessment Example

THREAT: SQL Injection in Login Form

| Factor | Score | Justification |
|--------|-------|---------------|
| Damage | 9 | Full database access, credential theft |
| Reproducibility | 9 | Consistent, automated tools exist |
| Exploitability | 8 | Well-documented attack, easy tools |
| Affected Users | 10 | All users with accounts |
| Discoverability | 7 | Scanners detect easily |

DREAD Score: (9+9+8+10+7)/5 = 8.6
Risk Level: CRITICAL
Priority: Immediate remediation required

---

Data Flow Diagrams

Visual representation of system data movement for security analysis.

DFD Elements

DFD Levels

Example: E-Commerce DFD

LEVEL 0: CONTEXT DIAGRAM

                              ┌──────────────────┐
                              │                  │
     ┌────────────┐           │   E-Commerce     │           ┌────────────┐
     │            │  Orders   │    System        │  Payment  │            │
     │  Customer  │──────────▶│                  │──────────▶│  Payment   │
     │            │◀──────────│                  │◀──────────│  Gateway   │
     └────────────┘  Status   │                  │  Result   └────────────┘
                              │                  │
                              └──────────────────┘
                                      │
                                      │ Fulfillment
                                      ▼
                              ┌────────────────┐
                              │   Warehouse    │
                              │    System      │
                              └────────────────┘


LEVEL 1: EXPANDED VIEW

┌─────────────────────────────────────────────────────────────────────┐
│                         TRUST BOUNDARY                               │
│  - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -  │
│                                                                      │
│   ┌─────────┐       ┌─────────┐       ┌─────────┐       ┌─────────┐ │
│   │         │       │   Web   │       │  Order  │       │ Payment │ │
│   │   CDN   │──────▶│ Server  │──────▶│ Service │──────▶│ Service │ │
│   │         │       │         │       │         │       │         │ │
│   └─────────┘       └────┬────┘       └────┬────┘       └────┬────┘ │
│                          │                 │                 │      │
│                          │                 │                 │      │
│                          ▼                 ▼                 ▼      │
│                    ╔═══════════╗     ╔═══════════╗    ╔═══════════╗ │
│                    ║  Session  ║     ║  Orders   ║    ║  Payment  ║ │
│                    ║   Store   ║     ║    DB     ║    ║    DB     ║ │
│                    ╚═══════════╝     ╚═══════════╝    ╚═══════════╝ │
│                                                                      │
└─────────────────────────────────────────────────────────────────────┘
                          │
                          │ Crosses Trust Boundary
                          ▼
                    ┌───────────┐
                    │  Payment  │
                    │  Gateway  │
                    │ (External)│
                    └───────────┘

Trust Boundary Analysis

---

Common Attack Patterns

OWASP Top 10 Mapping

Attack Pattern Catalog

ATTACK PATTERN: SQL Injection (A03)

Threat: T (Tampering), E (Elevation of Privilege)

Attack Vector:
1. Identify input fields that construct SQL queries
2. Test for injection: ' OR '1'='1' --
3. Extract data: UNION SELECT password FROM users
4. Escalate: Execute stored procedures, write files

Detection:
- WAF rules for SQL patterns
- Prepared statement verification
- Database query logging

Mitigation:
- Parameterized queries (primary)
- Input validation (secondary)
- Least privilege database accounts
- Web application firewall

Test Cases:
- Single quote injection: '
- Boolean-based: ' OR 1=1 --
- Time-based: '; WAITFOR DELAY '0:0:5' --
- UNION-based: ' UNION SELECT NULL, username, password FROM users --

Threat Intelligence Integration

Tool Reference

Read this when you need the full flag tables, usage examples, and output-format details for the bundled scripts (threat_modeler.py, secret_scanner.py).

Scripts Overview

Threat Modeler Features:

• STRIDE analysis for any system component
• DREAD risk scoring
• Mitigation recommendations
• JSON and text output formats
• Interactive mode for guided analysis

Secret Scanner Features:

• Detects AWS, GCP, Azure credentials
• Finds API keys and tokens (GitHub, Slack, Stripe)
• Identifies private keys and passwords
• Supports 20+ secret patterns
• CI/CD integration ready

---

threat_modeler.py

Purpose: Performs STRIDE threat analysis on system components with DREAD risk scoring, mitigation recommendations, and structured reporting.

Usage:

python threat_modeler.py --component "User Authentication"
python threat_modeler.py --component "API Gateway" --assets "user_data,tokens" --json
python threat_modeler.py --component "Database" --output report.txt
python threat_modeler.py --interactive
python threat_modeler.py --list-threats

Flags:

Example:

$ python threat_modeler.py --component "API Gateway" --json --output api-threats.json
Report written to api-threats.json

Output Formats:

• Text (default): Structured report grouped by STRIDE category with risk scores, DREAD ratings, attack vectors, and mitigations.
• JSON (`--json`): Machine-readable object containing component, analysis_date, summary (counts by risk level), and threats array with full DREAD breakdown per threat.

---

secret_scanner.py

Purpose: Detects hardcoded secrets, API keys, credentials, and private keys in source code. Supports 20+ secret patterns across cloud providers (AWS, GCP, Azure), authentication tokens (GitHub, GitLab, Slack, Stripe, Twilio, SendGrid), cryptographic keys, and generic credential patterns. Exits with code 1 when critical or high findings are present, making it CI/CD-ready.

Usage:

python secret_scanner.py /path/to/project
python secret_scanner.py /path/to/file.py
python secret_scanner.py /path/to/project --format json --output report.json
python secret_scanner.py /path/to/project --severity critical
python secret_scanner.py --list-patterns

Flags:

Example:

$ python secret_scanner.py ./src --severity high --format json
{
  "target": "./src",
  "scan_date": "2026-03-21T10:30:00",
  "summary": { "total": 2, "by_severity": { "critical": 1, "high": 1, "medium": 0, "low": 0 } },
  "findings": [ ... ]
}

Output Formats:

• Text (default): Severity-grouped report showing pattern ID, file path with line number, masked match text, and remediation recommendation.
• JSON (`--format json`): Machine-readable object with target, scan_date, summary (counts by severity), and findings array. Each finding includes pattern_id, name, severity, file_path, line_number, matched_text (masked), and recommendation.