software-data-integrity-failures

name

software-data-integrity-failures

description

Security testing skill for OWASP A08:2025 — Software or Data Integrity Failures in Python web applications (FastAPI and Flask). Use this skill whenever the user wants to audit, detect, test, or fix integrity vulnerabilities including: insecure deserialization (pickle, yaml, jsonpickle, dill), mass assignment flaws, untrusted CDN/module inclusion, unsigned updates, CI/CD pipeline integrity, or Subresource Integrity (SRI) checks. Trigger this skill for ANY task involving pickle/deserialization security, CWE-502, CWE-915, CWE-829, CWE-345, or CWE-494, even if the user doesn't explicitly mention OWASP A08.

A08:2025 — Software or Data Integrity Failures

Overview

OWASP rank: #8 (2025) | CWEs covered: 14 | Avg incidence: 2.75%

This category focuses on failure to verify integrity of software, code, and data artifacts within your own environment — distinct from A03's upstream supply chain focus. The core concern is: can you trust what you're loading, deserializing, or executing?

Key distinction from A03 (Supply Chain):

• A03 = upstream integrity (your dependencies, CI/CD pipelines)
• A08 = runtime integrity (what you actually deserialize, include, or auto-update at runtime)

---

Critical Risk Areas for Python

1. Insecure Deserialization — The #1 Python-Specific Risk

pickle.loads() on untrusted input equals arbitrary code execution via __reduce__. This is the single most dangerous A08 vector in Python.

Dangerous functions/modules to flag:

Module / Function	Risk	Safe Alternative
pickle.loads() / pickle.load()	RCE via __reduce__	JSON + Pydantic
cPickle.loads()	Same as pickle	JSON + Pydantic
dill.loads()	Superset of pickle, same RCE risk	JSON + Pydantic
jsonpickle.decode()	Deserializes Python objects	json.loads() only
shelve.open()	Backed by pickle	Redis/SQL store
yaml.load(data, Loader=None)	Code execution via !!python/object	yaml.safe_load()
numpy.load(f, allow_pickle=True)	RCE via pickled arrays	allow_pickle=False
torch.load(f)	Pickle-backed by default	weights_only=True

Detection pattern (static analysis):

# Flag any of these patterns in source code
DANGEROUS_PATTERNS = [
    r'pickle\.loads?\(',
    r'cPickle\.loads?\(',
    r'dill\.loads?\(',
    r'jsonpickle\.decode\(',
    r'shelve\.open\(',
    r'yaml\.load\([^)]*(?!safe_load)',
    r'numpy\.load\([^)]*allow_pickle\s*=\s*True',
    r'torch\.load\([^)]*(?!weights_only\s*=\s*True)',
]

If pickle is unavoidable — HMAC integrity check:

import hmac, hashlib, pickle, os

SECRET = os.environ["PICKLE_HMAC_SECRET"].encode()

def safe_serialize(obj) -> bytes:
    data = pickle.dumps(obj)
    mac = hmac.new(SECRET, data, hashlib.sha256).digest()
    return mac + data  # prepend 32-byte MAC

def safe_deserialize(payload: bytes):
    mac, data = payload[:32], payload[32:]
    expected = hmac.new(SECRET, data, hashlib.sha256).digest()
    if not hmac.compare_digest(mac, expected):
        raise ValueError("Integrity check failed — data tampered")
    return pickle.loads(data)

---

2. Mass Assignment (CWE-915)

Occurs when request.json or form data is passed directly into ORM .update() without field filtering — allowing attackers to set fields like is_admin=True or role="superuser".

Flask — Vulnerable pattern:

# ❌ DANGEROUS — attacker can inject any field
user = User.query.get(user_id)
user.__dict__.update(request.json)
db.session.commit()

# Also dangerous:
User.query.filter_by(id=user_id).update(request.json)

Flask — Safe pattern:

# ✅ SAFE — explicit allowlist
ALLOWED_FIELDS = {"name", "email", "bio"}
data = {k: v for k, v in request.json.items() if k in ALLOWED_FIELDS}
User.query.filter_by(id=user_id).update(data)

FastAPI — Pydantic prevents mass assignment by default:

# ✅ FastAPI with Pydantic — only declared fields accepted
class UserUpdate(BaseModel):
    name: str | None = None
    email: str | None = None
    bio: str | None = None
    # is_admin, role etc. cannot be set — they're not in the schema

@router.patch("/users/{user_id}")
async def update_user(user_id: int, body: UserUpdate, db: Session = Depends(get_db)):
    db.query(User).filter(User.id == user_id).update(body.model_dump(exclude_unset=True))

Detection pattern: In Flask, scan for request.json or request.form flowing into .update(), __dict__.update(), or ORM bulk-update calls without an intermediate allowlist filter.

---

3. Subresource Integrity (SRI) for External Scripts/Styles

If your Flask/FastAPI app renders HTML templates that load scripts or stylesheets from external CDNs, those resources must include SRI hashes to prevent CDN compromise from injecting malicious code.

Vulnerable Jinja2 template:

<!-- ❌ No integrity check — CDN compromise = XSS -->
<script src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"></script>

Secure Jinja2 template:

<!-- ✅ SRI hash prevents tampered CDN delivery -->
<script
  src="https://cdn.jsdelivr.net/npm/bootstrap@5.3.0/dist/js/bootstrap.bundle.min.js"
  integrity="sha384-geWF76RCwLtnZ8qwWowPQNguL3RmwHVBC9FhGdlKrxdiJJigb/j/68SIy3Te4Bkz"
  crossorigin="anonymous">
</script>

How to generate SRI hashes:

# Using openssl
curl -sL https://cdn.example.com/lib.js | openssl dgst -sha384 -binary | openssl base64 -A

# Or use srihash.org / unpkg.com automatically provides them

Detection: Scan Jinja2/HTML templates for <script src="https://..."> and <link href="https://..."> tags missing integrity= attributes.

---

4. Auto-Update Without Verification (CWE-494)

Applications that download and execute code/config updates without verifying a cryptographic signature are vulnerable to man-in-the-middle attacks.

Pattern to flag:

# ❌ Downloads and executes without verification
import urllib.request, subprocess
urllib.request.urlretrieve("https://example.com/update.sh", "/tmp/update.sh")
subprocess.run(["bash", "/tmp/update.sh"])

# ❌ Loads remote config without integrity check
import json, urllib.request
config = json.loads(urllib.request.urlopen("https://config.example.com/settings.json").read())

Safe pattern — signature verification:

from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import padding
import urllib.request

def download_and_verify(url: str, sig_url: str, public_key_path: str) -> bytes:
    data = urllib.request.urlopen(url).read()
    sig = urllib.request.urlopen(sig_url).read()
    with open(public_key_path, "rb") as f:
        pubkey = serialization.load_pem_public_key(f.read())
    pubkey.verify(sig, data, padding.PKCS1v15(), hashes.SHA256())  # raises on failure
    return data

---

Testing Checklist

Use this checklist when auditing a FastAPI or Flask application for A08 compliance:

Deserialization

• No pickle.loads() / pickle.load() on untrusted input
• No cPickle, dill, jsonpickle.decode() on untrusted input
• yaml.safe_load() used instead of yaml.load()
• numpy.load() called with allow_pickle=False
• torch.load() called with weights_only=True
• shelve not used with untrusted keys/data
• If pickle unavoidable: HMAC pre-verification implemented

Mass Assignment

• Flask: no request.json passed directly into ORM .update() without allowlist
• Flask: no model.__dict__.update(untrusted_data) patterns
• FastAPI: Pydantic models used for all request bodies (prevents by default)
• Privileged fields (is_admin, role, balance) not in public update schemas

External Integrity

• All external <script> tags have integrity= SRI attributes
• All external <link rel="stylesheet"> tags have integrity= SRI attributes
• Remote downloads verified with cryptographic signature or hash before execution
• No dynamic eval() or exec() on remotely fetched content

CI/CD Integrity (boundary with A03)

• Code review required for all changes to deployment config/scripts
• Build artifacts signed and signature verified before deployment
• CI/CD secrets not accessible to pull request pipelines from forks

---

Automated Scanning Tools

Tool	What it catches	How to run
Bandit	pickle, yaml.load, subprocess, eval	bandit -r . -t B301,B302,B506
Semgrep	Deserialization, mass assignment patterns	semgrep --config=p/python
pip-audit	Known-vulnerable deserialization libs	pip-audit
Manual grep	Quick sweep for dangerous functions	See patterns in section 1

Bandit rule IDs for A08:

• B301 — pickle usage
• B302 — marshal usage (similar risk)
• B506 — yaml.load without safe loader
• B403 — pickle import (informational)

---

Key CWEs

CWE	Description	Primary Vector
CWE-502	Deserialization of Untrusted Data	pickle, yaml, jsonpickle
CWE-915	Improperly Controlled Modification of Dynamically-Determined Object Attributes	Mass assignment
CWE-829	Inclusion of Functionality from Untrusted Control Sphere	CDN scripts without SRI
CWE-345	Insufficient Verification of Data Authenticity	Missing HMAC/signature checks
CWE-494	Download of Code Without Integrity Check	Auto-update without verification

---

References

• OWASP A08:2025 — Software and Data Integrity Failures
• Python Pickle Security
• SRI Hash Generator
• Bandit SAST for Python
• See references/deserialization-exploits.md for exploit PoCs and deeper analysis