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Maximilian Richter

Security Engineer

About

Software Developer focused on Security & AI. Recent graduate (Jan 2026) with professional experience at Bosch.

Current focus: AI Agent Robustness and Prompt Injection defense — understanding attack surfaces in emerging systems where traditional security models don't cleanly apply.

I prioritize hands-on security engineering over theoretical frameworks. I learn quickly, build deliberately, and value long-term consistency over short-term wins.

Philosophy

  • Secure systems over features
  • Real-world attack paths
  • Clarity through tooling
  • Practical over theoretical

Projects

Flagship Project

AEGIS

Security evaluation & policy framework for analyzing system and LLM behavior

Python • FastAPI • SQLite • Next.js • Docker • CI/CD

Problem

Traditional security tools lack visibility into LLM and agentic system behavior. Existing solutions focus on static analysis or basic prompt filtering, missing runtime policy violations, unexpected tool usage, and behavioral anomalies in production systems.

Approach

Trace-based evaluation framework that captures execution context, tool invocations, and decision paths. Policy engine evaluates behavior against defined rules with full audit trails. Designed for deterministic, reproducible security assessments.

Key Features

  • Real-time detection engine with rule-based pattern matching
  • Event normalization pipeline for heterogeneous log sources
  • Evidence collection and alert triage dashboard
  • Comprehensive test coverage with fuzzing and property-based testing
  • Production-ready CI/CD with security scanning and supply chain verification

Engineering Focus

Explainability Full trace context for every detection
Determinism Reproducible evaluations with versioned policies
Production-Ready Docker containerization, CI/CD, comprehensive testing

Dashboard

AEGIS Security Command Center Dashboard

Security Command Center showing real-time analytics, incident trace logs, and event distribution patterns

Additional Projects

Attack Surface Scanner

Python • Certificate Transparency • TLS

Non-intrusive attack surface scanner for SaaS environments. Passive asset discovery via Certificate Transparency logs, TLS configuration analysis, security header validation, and deterministic risk scoring.

Engineering Highlights

  • Zero-touch reconnaissance using CT logs and passive DNS
  • TLS cipher suite analysis with known-weak detection
  • Deterministic scoring model for consistent risk assessment
View Repository

Cloud Signal Engine

Python • FastAPI • Detection Engineering

Production-style security detection and abuse monitoring system. Ingests and normalizes logs, runs rule-based detections, generates alerts with evidence, and provides triage UI.

Engineering Highlights

  • Event normalization pipeline for heterogeneous sources
  • Rule-based detection engine with pattern matching
  • Evidence-based alerting with full context preservation
View Repository

Cloud Pentest Lab

Terraform • Python • AWS • IAM

Reproducible AWS penetration testing environment with intentionally vulnerable configurations. Demonstrates realistic attack chains from initial access to privilege escalation.

Engineering Highlights

  • Infrastructure-as-code for reproducible vulnerable environments
  • Multi-stage attack chain: S3 exposure → SSRF → privilege escalation
  • Automated teardown to prevent cost accumulation
View Repository

WebSec Playground

Flask • OWASP • Web Security

Deliberately vulnerable web application demonstrating OWASP Top 10 vulnerabilities. Educational resource for understanding common web security flaws and exploitation techniques.

Engineering Highlights

  • Realistic implementations of SQLi, XSS, IDOR, and CSRF
  • Isolated environment with clear exploitation paths
  • Educational focus with vulnerability explanations
View Repository

Technical Stack

Languages

Python TypeScript Solidity HCL

Security

TLS Analysis Certificate Transparency OWASP AWS Security Smart Contract Auditing

Cloud & Infrastructure

AWS IAM Terraform Docker

DevOps & Tools

GitHub Actions CI/CD Pydantic Flask boto3

Discipline & Performance

Long-term consistency, performance under stress, structured execution.

🏓

Youth Table Tennis Coach

Leadership, responsibility, and mentoring. Teaching technique and competitive mindset to young athletes.

🏊

Ironman 70.3 — 2026

Long-distance endurance preparation. 1.9km swim, 90km bike, 21.1km run. Sustained effort and recovery management.

🏃

Berlin Marathon — 2026

42.2km of execution. Structured training blocks, progressive overload, and race-day discipline.