Case studies

Real security results

Anonymized examples of how ZeroSight360 helps companies discover critical vulnerabilities and build secure systems — before attackers find the gaps.

Fintech
E-commerce
AI / SaaS

Fintech VAPT, API Security, Authentication

Series A Fintech — Critical Auth Bypass Discovery

Critical Findings: 3
Time to Fix: 10 days
Post-Launch Incidents: 0

Challenge

Pre-launch security validation needed within 2 weeks for a fintech processing $2M+ monthly transactions.

What we did

Full VAPT of web application, REST API, and authentication flows. Manual testing of business logic, session management, and payment integrations.

Result

All vulnerabilities remediated before launch. Zero security incidents in 8+ months post-launch.

Key findings

Authentication bypass via JWT manipulationCriticalCVSS 9.8
IDOR on payment endpoints exposing user dataCriticalCVSS 9.1
Privilege escalation through role parameter tamperingCriticalCVSS 9.4
Session fixation in OAuth callback flowHighCVSS 7.5
Rate limiting absent on sensitive endpointsHighCVSS 7.2

Critical 3High 8Medium 12

E-commerce Cloud Security, AWS, Infrastructure

E-commerce Platform — Cloud Security Transformation

Attack Surface Reduction: 94%
Public Buckets Closed: 4
Compliance Status: Ready

Challenge

AWS infrastructure grown organically over 3 years with 500K+ users. No formal security review ever conducted.

What we did

Comprehensive cloud security assessment: IAM audit, S3 bucket review, RDS encryption check, container hardening, WAF configuration, and network segmentation review.

Result

94% reduction in attack surface. Achieved compliance readiness. Security monitoring implemented.

Key findings

Overprivileged IAM roles with admin accessHighCVSS 8.6
4 public S3 buckets with sensitive data exposureCriticalCVSS 9.1
Unencrypted RDS instances with PII dataHighCVSS 7.8
No WAF protection on public-facing endpointsMediumCVSS 6.5
Exposed EC2 metadata endpoints (SSRF vector)HighCVSS 8.2

Critical 1High 6Medium 4Low 2

AI / SaaS AI Security, Architecture Review, LLM

SaaS Startup — AI Agent Security Architecture

Injection Vectors Fixed: 12
Data Leaks Prevented: 100%
SOC 2 Status: Ready

Challenge

AI-first SaaS building autonomous agents that handle sensitive customer data. Needed secure architecture before SOC 2 audit.

What we did

Threat modeling for LLM-powered agents, prompt injection testing, data flow review, embedding security analysis, and secure deployment architecture design.

Result

Secure-by-design AI system deployed. SOC 2 Type II readiness achieved. No data leakage incidents.

Key findings

Prompt injection allowing data exfiltrationCriticalCVSS 9.3
Data leakage via embedding similarity queriesHighCVSS 8.1
Inadequate agent sandboxing — filesystem accessHighCVSS 8.7
Excessive agent permissions (write to production DB)HighCVSS 7.9
Missing input sanitization on user promptsMediumCVSS 6.8

Critical 1High 5Medium 4Low 2

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