Samratth Singh — adversarial ML, multi-agent robustness, and behavioral threat modeling. The work hunts for the structural condition that makes a whole class of failure inevitable, not a single clever exploit.
Open to research collaborationA structural taxonomy of LLM adversarial surfaces — twelve categories spanning injection, jailbreaking, evasion, and extraction. A map of failure conditions, not an exploit chain.
PublishedQMIX-based multi-agent RL for drone-swarm jammer avoidance at DIAT (DRDO). Identified emergent policy decoupling and traced it to mechanism via a KL-anchored Navigator. Submitted to IEEE TNNLS.
In reviewHybrid LSTM/Diffusion synthesis of human mouse trajectories, studied as a behavioral-biometric adversarial primitive. Probes the limits of motion-based bot detection. Target: IEEE TIFS.
In progressA custom PyTorch MLP phishing detector engineered from scratch — F1 0.9484 — deployed on Hugging Face and past 60 downloads with zero marketing. Detection as a shipped artifact, not a notebook.
60+ downloadsAutomated threat modeling and attack-surface enumeration for LLM-integrated enterprise deployments. Maps adversarial pathways, generates attack trees, quantifies exposure across boundaries.
BuildingI work at the intersection of AI security, multi-agent systems, and applied cryptography — three domains sharing one question: what makes an entire category of system exploitable?
That question produced a taxonomy of LLM adversarial surfaces (Best Paper, ICEM 2024), an ongoing MARL study of emergent coordination failure in drone swarms at DIAT (DRDO), and a behavioral-biometric adversarial primitive. CTF results are execution evidence — not the headline.
Final-year Computer Science (Cybersecurity) at DY Patil International University, Pune. Chair of the ACM Student Chapter. Currently building toward IEEE Q1 publication and international graduate study.
Modelling attack surface as a hypergraph, with quantum-walk sampling as a traversal method for non-obvious pivots — now being developed as EVELYN, applying quantum-walk graph ML to phishing infrastructure fingerprinting.
QMIX-based multi-agent RL for jammer avoidance. Identified emergent policy decoupling through behavioral observation and traced it to mechanism. Building toward IEEE Q1 submission.
Secure infrastructure, VPN architecture, OWASP vulnerability remediation, and zero-trust implementation across production API surfaces.
Developed the ICEM taxonomy of LLM adversarial surfaces. Best Paper and Best Presentation at ICEM 2024 and NCTAAI 4.0.
National-ranked CTF competitor across Ciphathon, HackTheBox, TryHackMe, and SecLeaf. Builder of RAV3N-SEC and T.E.M.P.E.S.T.