Enhance your defenses with DSecured's unparalleled expertise in Python penetration testing. With years of experience and numerous successful engagements, our detailed and insightful reports empower IT departments to effectively address vulnerabilities. Join our multitude of satisfied customers and ensure your organization’s security. Trust DSecured for reliable, comprehensive solutions.
What we test in Python projects
Django ORM security, Flask Blueprints, template engines (Jinja2 SSTI), middleware security and custom authentication.
Pickle deserialization RCE, model poisoning, data leakage in Jupyter Notebooks and API security for ML endpoints.
FastAPI, Django REST Framework, GraphQL security, authorization issues and mass assignment vulnerabilities.
Python is the leading language for data science, ML/AI and modern web APIs and powers critical backend systems - from Django enterprise apps to FastAPI microservices and machine learning pipelines. This massive adoption makes Python a preferred target: Pickle deserialization RCE, SQL injections in Django ORM, Jinja2 SSTI, command injection via subprocess and vulnerable pip dependencies regularly lead to critical vulnerabilities - from data breaches to model poisoning and full server takeovers.
Pickle Deserialization & Command Injection Insecure Pickle deserialization leads to RCE, subprocess calls with user input enable command injection - Python's flexibility becomes a vulnerability with insecure input processing.
Django ORM & SQL Injection Bypasses Django ORM protects against standard SQL injection, but .raw(), .extra() and custom queries are vulnerable. Mass assignment via ModelForms and authorization bypasses are classic issues.
Jinja2 SSTI & Template Injection Flask/Django template injection (SSTI) leads to RCE - {{config}}, render_template_string() with user input and insecure Jinja2 filters are critical risks in the Python web stack.
We deliver prioritized results with PoC code, concrete fix suggestions for your dev team and - if desired - management summaries for stakeholders and compliance audits.
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A large part of the internet is based on websites and web applications.
Modern websites and SPAs usually communicate with some kind of API.
Fully automated vulnerability scanning for your IT infrastructure or application.
Python penetration tests uncover a wide spectrum of vulnerabilities - from Pickle deserialization RCE to Jinja2 SSTI to Django ORM bypasses, command injection and OWASP Top 10.
Insecure Pickle deserialization via pickle.loads() with user input leads to remote code execution. YAML deserialization (yaml.load()), Marshal and Shelve are also critical RCE vectors in Python.
subprocess.call(), os.system() and os.popen() with user input enable command injection. Shell injection via shell=True and insecure string formatting are Python-specific risks.
Server-side template injection in Jinja2 - especially via render_template_string() with user input, insecure custom filters and template sandboxing bypasses lead to RCE in Flask/Django.
Django ORM is usually secure, but .raw(), .extra() and custom SQL queries are vulnerable. Mass assignment via ModelForms, ORM query bypasses and authorization issues in Django REST Framework.
Path traversal via open(), os.path.join() without sanitization, file upload bypasses and directory traversal in Flask send_file() - Python's flexible file handling is vulnerable with insecure input validation.
Python's huge ecosystem (PyPI) is vulnerable to known CVEs - vulnerable packages (requests, urllib3, Django, Flask) regularly lead to exploits. pip audit only finds a fraction.
The price depends on complexity - simple Flask REST APIs vs. enterprise Django apps with ML pipelines, complex authorization flows and extensive pip dependencies.
For simple Flask/FastAPI REST APIs
For Django Enterprise & ML/AI Apps
Our Mini Pentest for Python focuses on pickle deserialization, SSTI in Jinja2/Flask, unsafe eval() calls and OS command injections. Ideal for data science APIs, Flask/FastAPI services or automation scripts.
Focused examination of the most critical vulnerabilities
Transparent fixed price - no hidden costs
Fast, actionable reporting as ticket list
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We've had the privilege of working with some of the world's leading companies and strengthening their IT security.
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Direct contact with our experts
A Python penetration test focuses on identifying vulnerabilities that specifically occur in Python-based applications, such as input validation issues, insecure libraries and critical errors in frameworks. Our approach aims to uncover and assess these specific risks.
The duration of a Python penetration test depends heavily on the complexity and scope of the application being tested, but we typically plan between one and three weeks for comprehensive assessments. This includes both test execution and reporting.
A Python penetration test offers significant benefits, such as uncovering hidden security gaps, preventing data theft and reducing the risk of serious security breaches. Additionally, they increase your customers' confidence in the security of your products.
Our experts conduct the test in a controlled environment and coordinate precautions with you in advance to ensure that normal operation of your Python application is not disrupted during the test.
After completing the penetration test, we provide you with a comprehensive report that includes an executive summary, detailed technical insights and concrete steps for remediation of discovered vulnerabilities, all presented in an understandable manner.
A Python security test is highly flexible and suitable for both web applications and desktop and server applications based on Python. We adapt our testing strategies to the specific deployment area.
While our primary goal is to identify vulnerabilities, we also offer support in implementing new security measures after evaluating the test results. This includes both technical improvements and strategic consultation.
We recommend conducting Python security tests regularly, especially after major code changes, after adding new features, or at least once a year as part of a comprehensive security strategy.
It is helpful if your team understands that access to internal knowledge may be necessary during the test. Good communication and willingness to collaborate are important to optimize results and ensure that all relevant risks are assessed.
The data collected during the tests is treated strictly confidentially. All information we receive during the test is protected according to the highest security standards and used only for direct improvement of your application security.
