research

Synaptic Adversarial Intelligence Introduction

August 23, 2022

Table of Contents

Share:

It is my great pleasure to announce the formation of HiddenLayer’s Synaptic Adversarial Intelligence team, SAI.

First and foremost, our team of multidisciplinary cyber security experts and data scientists are on a mission to increase general awareness surrounding the threats facing machine learning and artificial intelligence systems. Through education, we aim to help data scientists, MLDevOps teams and cyber security practitioners better evaluate the vulnerabilities and risks associated with ML/AI, ultimately leading to more security conscious implementations and deployments.

Alongside our commitment to increase awareness of ML security, we will also actively assist in the development of countermeasures to thwart ML adversaries through the monitoring of deployed models, as well as providing mechanisms to allow defenders to respond to attacks.

Our team of experts have many decades of experience in cyber security, with backgrounds in malware detection, threat intelligence, reverse engineering, incident response, digital forensics and adversarial machine learning. Leveraging our diverse skill sets, we will also be developing open-source attack simulation tooling, talking about attacks in blogs and at conferences and offering our expert advice to anyone who will listen!

It is a very exciting time for machine learning security, or MLSecOps, as it has come to be known. Despite the relative infancy of this emerging branch of cyber security, there has been tremendous effort from several organizations, such as MITRE and NIST, to better understand and quantify the risks associated with ML/AI today. We very much look forward to working alongside these organizations, and other established industry leaders, to help broaden the pool of knowledge, define threat models, drive policy and regulation, and most critically, prevent attacks.

Keep an eye on our blog in the coming weeks and months, as we share our thoughts and insights into the wonderful world of adversarial machine learning, and provide insights to empower attackers and defenders alike.

Happy learning!

Tom Bonner

Sr. Director of Adversarial Machine Learning Research, HiddenLayer Inc.

Related Research

Research
xx
min read

Exploring the Security Risks of AI Assistants like OpenClaw

OpenClaw (formerly Moltbot and ClawdBot) is a viral, open-source autonomous AI assistant designed to execute complex digital tasks, such as managing calendars, automating web browsing, and running system commands, directly from a user's local hardware. Released in late 2025 by developer Peter Steinberger, it rapidly gained over 100,000 GitHub stars, becoming one of the fastest-growing open-source projects in history. While it offers powerful "24/7 personal assistant" capabilities through integrations with platforms like WhatsApp and Telegram, it has faced significant scrutiny for security vulnerabilities, including exposed user dashboards and a susceptibility to prompt injection attacks that can lead to arbitrary code execution, credential theft and data exfiltration, account hijacking, persistent backdoors via local memory, and system sabotage.

Research
xx
min read

Agentic ShadowLogic

Agentic ShadowLogic is a sophisticated graph-level backdoor that hijacks an AI model's tool-calling mechanism to perform silent man-in-the-middle attacks, allowing attackers to intercept, log, and manipulate sensitive API requests and data transfers while maintaining a perfectly normal conversational appearance for the user.

Research
xx
min read

MCP and the Shift to AI Systems

HiddenLayer’s AI Runtime Security addresses the critical "visibility gap" in the Model Context Protocol (MCP) by monitoring the behavior of autonomous agents in real-time to detect and block unauthorized tool use or data exfiltration.

Stay Ahead of AI Security Risks

Get research-driven insights, emerging threat analysis, and practical guidance on securing AI systems—delivered to your inbox.