KEERIT
KAPOOR

CYBERSECURITY & CODE — TMU

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CHECKPOINT 01 / PROFILE

ABOUT

I’m Keerit. I’m exploring how systems work, where they break, and how to make them safer. My work brings together hands-on security analysis, Python code reviews and clear explanations of technical risks.

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Building my next projects and deepening my understanding of network and application security.

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● ● ● secure_app.py
# safer defaults
def load_preferences(data):
    result = json.loads(data)
    if not isinstance(result, dict):
        raise ValueError("Expected object")
    return result
ILLUSTRATIVE SUMMARY / PYTHON SECURITY REVIEW
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CHECKPOINT 02 / PROJECT

Secure Python review

From an intentionally vulnerable Python demo to a hardened rewrite, with every security decision documented.

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Reviewed a local Python demo through manual inspection and Bandit static analysis. Documented hard-coded credentials, weak password hashing, unsafe deserialization and shell-command injection risks.

PythonBanditSecure coding
ASSESSMENT COMPLETETRANSPORT · HEADERS · EXPOSURE
01 / OBSERVE
02 / VALIDATE
03 / DOCUMENT
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CHECKPOINT 03 / PROJECT

Web vulnerability assessment

A careful look at transport security, browser protections and information exposure in a public training environment.

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Assessed a public security-training website using passive OWASP ZAP analysis, browser inspection and limited Nmap service identification. Prioritized evidence, risk classification and practical remediation.

NmapOWASP ZAPHTTP
REQUEST / RESPONSEGET /users
{
  "data": [...],
  "access": "review",
  "principle": "least privilege"
}
PUBLIC DEMO / PRODUCTION RISK SCENARIOS
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CHECKPOINT 04 / PROJECT

API security risk analysis

Exploring data minimisation and access controls through a non-intrusive review of a public demo API.

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Reviewed JSONPlaceholder response data and visible request headers to explain how API design decisions can create risks in production systems.

API securityDevToolsRisk analysis
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CHECKPOINT 05 / PROJECT

Phishing detection & awareness

Making email threats easier to recognise through side-by-side examples and practical prevention guidance.

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Compared simulated safe, suspicious and phishing messages, examining sender domains, urgency, links and requests for sensitive information.

Email analysisSocial engineering
HUMAN FIREWALL / AWARENESSTHINK.
CHECK.
PROTECT.
RECOGNISE → RESPOND → REPORT
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CHECKPOINT 06 / PROJECT

Phishing awareness training

An approachable training module that turns phishing red flags into everyday security habits.

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Developed a training presentation covering impersonation, lookalike domains, social engineering and safer handling of messages and websites.

TrainingCommunicationAwareness

CHECKPOINT / COMPLETE

FIN

MORE TO COME ■

A collection by Keerit Kapoor.
Built with curiosity. Still evolving.

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