AI Security Researcher & Red Teamer focused on adversarial attacks against LLMs, autonomous AI agents, and RAG pipelines. Prompt injection, goal hijacking, memory poisoning — I find the edge cases before attackers do.
About me
I'm a Brazilian security researcher focused on one of the most under-explored frontiers in offensive security: adversarial attacks against AI systems. While the industry races to deploy LLMs and autonomous agents, I research how they break.
My work sits at the intersection of red teaming and AI safety — finding prompt injection vectors, goal hijacking in tool-calling agents, poisoning RAG knowledge bases, and exposing how seemingly small inputs can cascade into catastrophic model behavior.
Before pivoting to AI security, I built a foundation in malware analysis, traditional pentesting, and purple team operations. That adversarial mindset now applies directly to AI systems — the attack surface just got a lot more interesting.
Capabilities
Systematic adversarial testing of LLMs and autonomous AI agents. Finding jailbreaks, prompt injections, goal hijacking vectors, and emergent failure modes before deployment.
Attacking multi-step agentic systems — manipulating memory, poisoning tool outputs, and inducing unintended action chains in ReAct and function-calling architectures.
Static and dynamic analysis of malicious binaries, unpacking, deobfuscation, and behavioral profiling. Reverse engineering with a focus on C/C++ and Python-based threats.
Web and network penetration testing — identifying and exploiting vulnerabilities across applications, APIs, and infrastructure. From recon to post-exploitation reporting.
Bridging offensive findings with defensive improvements. Working with blue teams to translate attack paths into detection rules, response playbooks, and architecture hardening.
Work
An open-source framework for systematically red teaming LLMs and autonomous AI agents. Covers prompt injection, goal hijacking, jailbreak catalogues, RAG poisoning, tool misuse, and memory attacks. Built for security researchers and AI teams who need structured, reproducible adversarial testing workflows.
from ai_redteam import AgentAttacker
# Goal hijacking via tool injection
attacker = AgentAttacker(
target="gpt-4o-tools",
attack_type="goal_hijack"
)
result = attacker.inject(
payload=hijack_payload,
via="memory_store"
)
# Evaluate success rate
attacker.report(result)Team blog for a CTF group, built with Jekyll for write-up publishing. Custom front-end with interactive components and dark cyber aesthetic.
Detailed write-ups of real AI red team engagements — prompt injection chains, agent goal hijacking, and RAG poisoning walkthroughs. Published as reproducible research.
Career
Building the AI Red Teaming Toolkit, conducting adversarial research on LLMs and agentic systems, and consulting for teams deploying AI in production environments.
Tier-1 support and network troubleshooting across multiple ISPs. Built a foundation in system administration and network diagnostics.
Active CTF competitor focusing on web, reverse engineering, and forensics challenges. Founded the team's public write-up blog.
Formation
Fluent English. Continuous learning across AI security, adversarial ML, and offensive tooling research.
Contact
Whether you're looking to red team your AI product, need a security review of an LLM integration, or want to collaborate on adversarial AI research — reach out.