Offensive Security * AI Red-Team * Security Engineering
Security engineer at the rare intersection of hands-on offense and applied machine learning -- full-scope pentesting, red-team tooling, and AI/LLM red-teaming.
M.Eng Cybersecurity, University of Maryland. I turn security work into code so finding and fixing vulnerabilities scales instead of depending on one specialist by hand.
// about
I work across the full offensive-security lifecycle -- reconnaissance, web/API/network/cloud exploitation, privilege escalation, lateral movement, and post-exploitation -- and I write the Python and Bash automation that makes that repeatable. On top of classic offense I have genuine AI/ML-security depth: adversarial ML, ML-pipeline attacks, and LLM attack-surface analysis, plus a 97%-accuracy anomaly-detection pipeline built from scratch.
Before graduate school I owned information security end to end for an LPG distributor protecting the data of roughly 30,000 customers -- hardening endpoints and networks, and detecting, investigating, and remediating real incidents. I own problems from first recon to prioritized, CVSS-scored remediation, and I translate findings into fixes engineers can actually ship.
Targeting: Penetration Tester * Red Team / Offensive Security Engineer * Application Security * AI/ML Security * Detection & Response * Vulnerability Research.
// capabilities
Grouped by discipline. Everything here maps to coursework, work experience, or shipped projects -- no filler.
// credentials
// selected work
Shipped security tooling plus hands-on red-team, ML-detection, and forensics work. The open-source tools below are public on GitHub with tests and CI; the rest each shipped an artifact -- a report, a pipeline, or a recovered payload.
A coherent IoT and firmware security toolkit, one tool per language, tracing the workflow from a raw image to a live device. Each is public, tested, and passing CI.
Static security auditor for extracted firmware root filesystems. Flags insecure permissions, hardcoded secrets, telnet and weak SSH config, outdated components, and embedded keys, sorted by severity with JSON output for CI gating. Tuned against a real OpenWrt image to cut false positives.
Sliding-window Shannon entropy scanner. Locates encrypted or compressed regions and embedded keys inside a binary by byte offset, with a terminal sparkline. Dependency-free C11 built on a rolling-window histogram.
MQTT broker security probe. Checks for anonymous access, plaintext transport, wildcard and $SYS topic exposure, and retained-message leakage, then reports findings by severity with a JSON mode. Verified end to end against live mosquitto brokers.
IoT device discovery and fingerprinting over SSDP, mDNS, and banner grabbing, mapping responses to device types. Table and JSON output; the parsers are covered by unit tests.
Led a 3-person red team from recon and OSINT through post-exploitation across a simulated enterprise (Windows, Ubuntu, AWS). Exploited services, dumped and cracked credentials, escalated privileges, moved laterally to domain admin, and exfiltrated data from a misconfigured S3 bucket.
Delivered CVSS-scored findings + prioritized remediation roadmap
Engineered an automated detection pipeline (Isolation Forest + LSTM Autoencoder) over user-activity logs on the CERT Insider Threat dataset, with no labeled attack data. Proposed SHAP explainability and federated-learning privacy mitigations.
97% detection accuracy, unsupervised
Reconstructed a full attack timeline: captured live C2 HTTP traffic, decoded a Base64 encryption key, and used a file-signature mismatch to uncover a VeraCrypt container disguised as an .mp3 -- then decrypted it to recover the payload.
Scored 30/30
Built a malware-similarity engine using assembly n-grams, MurmurHash3 feature hashing, and Jaccard similarity over Protobuf-parsed disassembly to cluster and classify related binaries.
Assembly-level family classification
Developed exploits across buffer overflow, format-string, and ROP-chain classes with custom shellcode, and hardened the same targets with SELinux -- practicing both sides of memory-corruption defense.
Exploit dev + hardening, end to end
Applied approximate agglomerative clustering with PCA dimensionality reduction to the KDD Cup network dataset to surface anomalous traffic clusters for intrusion detection.
20 anomalous clusters isolated
// timeline
// hack the box
// get in touch
Hiring for offensive, application, or AI security? I'm available immediately and open to relocating anywhere in the US.