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software-data-integrity-failures

software-data-integrity-failures

github.com/scholarly360Security2244 installsUpdated 29w ago
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About software-data-integrity-failures

A Python security-audit skill for developers reviewing software and data integrity risks in FastAPI and Flask applications. It focuses on runtime threats such as unsafe deserialization, mass assignment, unverified external resources, and insecure updates.

Key features

  1. 01Flags unsafe deserialization patterns
  2. 02Checks mass-assignment risks
  3. 03Reviews SRI for external assets
  4. 04Audits update and download verification
  5. 05Provides testing checklists and scan commands

Use cases

  1. 01Audit a Flask or FastAPI codebase
  2. 02Review untrusted data deserialization
  3. 03Check external scripts for integrity hashes
  4. 04Add integrity checks to update workflows

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/scholarly360/owasp-top10-web-skills --skill software-data-integrity-failures

After installing, type/software-data-integrity-failuresin Claude Code to confirm it loaded.

Bundled filesreferences/deserialization-exploits.md

Details

From the project README

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 / FunctionRiskSafe Alternative
pickle.loads() / pickle.load()RCE via __reduce__JSON + Pydantic
cPickle.loads()Same as pickleJSON + Pydantic
dill.loads()Superset of pickle, same RCE riskJSON + Pydantic
jsonpickle.decode()Deserializes Python objectsjson.loads() only
shelve.open()Backed by pickleRedis/SQL store
yaml.load(data, Loader=None)Code execution via !!python/objectyaml.safe_load()
numpy.load(f, allow_pickle=True)RCE via pickled arraysallow_pickle=False
torch.load(f)Pickle-backed by defaultweights_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/[email protected]/dist/js/bootstrap.bundle.min.js"></script>

Secure Jinja2 template:

<!-- ✅ SRI hash prevents tampered CDN delivery -->
<script
  src="https://cdn.jsdelivr.net/npm/[email protected]/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

ToolWhat it catchesHow to run
Banditpickle, yaml.load, subprocess, evalbandit -r . -t B301,B302,B506
SemgrepDeserialization, mass assignment patternssemgrep --config=p/python
pip-auditKnown-vulnerable deserialization libspip-audit
Manual grepQuick sweep for dangerous functionsSee 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

CWEDescriptionPrimary Vector
CWE-502Deserialization of Untrusted Datapickle, yaml, jsonpickle
CWE-915Improperly Controlled Modification of Dynamically-Determined Object AttributesMass assignment
CWE-829Inclusion of Functionality from Untrusted Control SphereCDN scripts without SRI
CWE-345Insufficient Verification of Data AuthenticityMissing HMAC/signature checks
CWE-494Download of Code Without Integrity CheckAuto-update without verification

References

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