About ByteShield
ByteShield was founded on a simple observation: the security industry is five years behind the AI engineering industry, and developers are paying the price.
Every week, production AI systems ship with prompt injection vulnerabilities, over-permissioned agents, and RAG pipelines leaking sensitive data — not because engineers are careless, but because the tooling, documentation, and security patterns simply don't exist yet.
ByteShield exists to build that foundation, one technical deep-dive at a time.
About the Founder
Rajat Khanna is a software engineer and AI security researcher specializing in the intersection of large language model (LLM) systems and application security.
His work focuses on four critical areas that define the emerging AI security landscape:
- Prompt injection attack patterns and prevention in production LLM applications
- Authorization and least-privilege design for autonomous AI agents
- Data leakage and access control vulnerabilities in Retrieval-Augmented Generation (RAG) systems
- AI-powered network anomaly detection and the security of ML inference pipelines
His technical writing at ByteShield covers the OWASP LLM Top 10 — the definitive industry standard for large language model security — translating its vulnerabilities into working code and practical defenses for engineers building production systems.
ByteShield's newsletter, The ByteShield Brief, delivers weekly AI security intelligence to engineers including engineers at notable companies worldwide.
Why ByteShield Exists
The OWASP LLM Top 10 was published in 2023. Most developers building with LLMs today have never read it.
Prompt injection — the #1 ranked vulnerability — is trivially exploitable in the majority of production AI applications. AI agents with excessive permissions can be manipulated into deleting data, exfiltrating credentials, or executing unauthorized actions.
These aren't theoretical risks. They are happening in production systems right now.
ByteShield covers these vulnerabilities with the depth they deserve: real attack code, real defenses, and real implementation patterns — not vendor whitepapers and not surface-level explainers.
What Makes ByteShield Different
Every post includes working code.
This is a deliberate editorial standard, not a stylistic choice. Security knowledge that can't be implemented is just trivia. ByteShield's code examples are tested, production-relevant, and immediately usable.
Topics covered here — AI agent authorization, RAG pipeline security, LLM input/output sanitization — have almost no existing technical literature. ByteShield is building that literature from scratch, informed by real systems and real attack patterns.