$ whoami
> ananya_chatterjee.exe
$ cat role.txt
> Security Engineer // AppSec · IAM Governance · AI Red Teaming
$ status
> [ONLINE] currently breaking LLM agents for fun and CVEs
+ 5 years deep in AppSec, IAM governance, and DevSecOps
+ Now hunting vulnerabilities in things that talk back: LLMs and AI agents
+ Currently building open-source tooling to make AI attack surfaces visible
- Not interested in "the model is a black box" as an acceptable answerI break things that talk in natural language for a living — prompt injection, leaky RAG pipelines, agents with way too much tool access. By day: IAM reviews, access governance, compliance automation. By night: making AI applications confess their architecture.
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Most AI security tools fuzz the chat box and call it a day. LLMASM goes after the architecture hiding behind it — hidden RAG proxies, leaked system prompts, agent tool chains nobody documented, shadow APIs that never made it into the docs. pip install llmasm-ananya
llmasm🧩 AST agent-graph mapping • 🕵️ shadow API fuzzing • ⚖️ LLM-as-a-judge verification • 🔁 native SARIF for CI/CD
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$ tail -f intentions.log
[+] mapping AI attack surfaces
[+] writing it up on Hashnode
[+] always down to talk red teaming, IAM, or both
[+] connect ↑ — DMs open
⭐ if you've made it this far, go star llmasm — it remembers


