Artículos de: Parth Deshmukh

Parth Deshmukh

Parth is a passionate and driven individual with strong skills in Python and JavaScript. With a solid background in statistics and financial mathematics, he is exploring opportunities in both full-stack development and quantitative roles.

The Agentic Threat to Finance: What Claude Mythos Reveals About Systemic Cyber Risk

Mythos marks a shift from AI as a tool to an autonomous attacker—rapidly discovering and exploiting vulnerabilities, especially in legacy systems, faster than defenders can respond, exposing a critical security gap.

Your AI Agents Deserve a Better Office Than a Terminal Window

AI agents collaborate visibly in a virtual office space

Claude Mythos and the Anthropic Leak: What the Capybara Incident Reveals About Frontier AI Security

Anthropic leak shows top AI firms can fail from simple misconfigurations, exposing assets and code, highlighting rising cyber risks and weak enforcement controls.

An AI Agent Started Mining Crypto in Secret. Here Is the Security Problem Nobody Is Talking About.

An AI agent built by an Alibaba affiliated lab quietly mined cryptocurrency on company infrastructure and hid it behind a secret network tunnel with no instructions, no hack, and no human awareness until security alerts fired. Around the same time, a Meta agent leaked proprietary code through valid credentials. Neither incident involved an attacker. That's exactly the problem.

LangChain Deep Agents: What the Architecture Means for AI Agent Security

Deep Agents don’t add new AI capability, they change the architecture. Planning, memory, filesystem storage, and subagents make agents more powerful but also expand the security surface. Stored content becomes a prompt injection risk, memory spans sessions, and agent handoffs create trust gaps. It’s more observable, but only secure if teams enforce strong controls around tools, storage, and monitoring.

AI Chatbot Logging Is a Security Problem: What the Sears Exposure Reveals

The Sears exposure shows that AI chatbot logging is a security risk, not just telemetry. Modern agents generate large volumes of sensitive data across text and voice, often stored without proper access control, retention limits, or session management. Voice data adds another layer of risk since it can be used for impersonation and fraud. Beyond data leakage, logs can also expose how the agent works, making it easier to reverse engineer or manipulate. The issue isn’t the breach itself, it’s treating AI logging like a feature instead of a regulated data system.

Meta’s Acquisition of Moltbook: What It Means for AI Agent Security

Meta’s acquisition of Moltbook highlights the growing importance of infrastructure for coordinating and identifying AI agents in the emerging “agentic web