Case Study: AI App Security Audit—Risks Found & Remediated
Client Background
A SaaS company in Marion, United States, partnered with Pentest Testing Corp to perform a comprehensive security review of their newly launched AI-driven web application. Their primary objective: identify and resolve vulnerabilities before launch.
Assessment Methodology
- Reconnaissance & Enumeration
Mapped all app endpoints, APIs, and model-serving interfaces for risk exposure. - Vulnerability Scanning & Penetration Testing
Targeted OWASP Top 10 threats, insecure authentication, prompt injection, adversarial attacks, and API flaws. - AI-Specific Security Checks
Tested for AI-related issues including model theft, data poisoning, input manipulation, and privacy risks. - Reporting & Remediation
Delivered a prioritized, actionable report with remediation guidance.
Key Security Issues Identified
1. Weak API Authentication
APIs for model inference and data exchange lacked strong authentication, risking unauthorized access.
Solution:
Deployed secure token-based API authentication, enabled rate limiting, and implemented detailed access logging.
2. Information Disclosure
Error messages and debug output revealed sensitive app internals.
Solution:
Configured generic error handling to prevent information leaks.
3. Insufficient Input Validation / Prompt Injection
AI endpoints were susceptible to prompt injection and malformed input, potentially manipulating outputs.
Solution:
Applied strict input validation, output encoding, and detection of suspicious queries.
4. Adversarial Input Risks
Model responses could be manipulated using crafted adversarial queries.
Solution:
Integrated adversarial input filters, enhanced model robustness, and enabled monitoring for anomalous activity.
5. Data Poisoning Threats
Risk of model compromise from tampered or malicious training data.
Solution:
Recommended secure data pipelines, integrity checks, and regular model validation.
6. Insecure Data Storage
Sensitive data lacked adequate encryption and access controls.
Solution:
Implemented encryption at rest and in transit, with strict permission controls.
7. Lack of Security Monitoring
No monitoring or alerting for suspicious or unauthorized actions.
Solution:
Established real-time security monitoring and defined incident response procedures.
Results & Client Feedback of the AI App Security Audit
The client received a detailed, prioritized remediation roadmap and implemented fixes with our guidance. They gave a 5-star review, praising the depth and clarity of the testing:
“Very thoroughly done.”
Why AI Applications Need Specialized Security
- Prompt injection and manipulation can leak data or control outputs
- Model theft threatens intellectual property
- Adversarial and poisoned inputs degrade trust and reliability
- Sensitive data exposure increases privacy and compliance risks
Proactive security assessment and remediation are vital.
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