Right now, there is probably an AI agent running on a laptop somewhere in your network. It stores API keys and database passwords, can access your internal systems, and works without anyone watching. Nobody reviewed its access. Nobody owns it. If an employee had that much reach with that little oversight, you would call it an insider risk. So why do we still call the agent a productivity tool?

Platform-as-a-Service (PaaS) provider Railway on Wednesday disclosed "anomalous activity" related to systems that store customer environment variables. Upon further investigation, the company found that an unauthorized third party may have gained access to encrypted environment variables, and, in some cases, obtained the decrypted contents. The impact, it said, is limited to a "subset of users,

Generative AI has changed what a malicious email looks like. Messages that once gave themselves away through clumsy wording, generic pretexts, and reused templates now arrive fluent, personalized, and unique to each recipient, and the behavioral signals email filters have relied on for years are fading as a result. Proofpoint shared several examples of the shift at its Protect 2026 conference.

An AppSec team can point an AI agent running on a general-purpose model at its own web apps with little setup. Sure, the model writes the attacks. But everything that makes those attacks safe to run against production and worth acting on afterward lives in the harness around the model. The harness is where the engineering in AI pentesting sits. The Hugging Face intrusion OpenAI disclosed in July

A sales manager builds an AI-assisted workflow to route leads. It connects to customer records, summarizes inbound requests, and posts updates to a shared channel. The workflow saves time, reduces manual work, and soon becomes part of the team’s daily routine. Across the same organization, employees are building useful apps, agents, and automations faster than existing processes can support them

In Salesforce Agentforce, the recommended way to make an agent respond to events is a Flow: a record is created or a case is updated, and the Flow passes data into the agent's prompt. Separately, Email-to-Case and Web-to-Case turn inbound customer messages into case records; it is how airlines and hotels run support intake. Both are standard, documented, as-designed features. Chained together,

In the space of a month, a new category of AI model went from nonexistent to everywhere. TypeSafe AI released Jev on September 15 and called it a System One model, a nod to Kahneman's fast, intuitive thinking. OpenAI announced its own Decisions API at DevDay and opened a public beta on October 6. Cribl published cribl-decision, an open LoRA adapter you can run locally. Others, like Laya, are

Attackers are impersonating popular AI products to distribute malware, steal browser sessions, and compromise business accounts. ANY.RUN linked 1,258 public sandbox analyses to fake AI campaigns between April and October of 2026.  The campaigns also increase operational pressure on MSSPs serving US businesses, which accounted for 16% of country-attributed targets.  Similar attacks

AI is already part of digital forensics and incident response work, with or without a policy in place to govern it. The issue is that these tools are growing more confident in their own output faster than practitioners are developing reliable ways to verify it. Without governance models and validation standards in place, that gap compounds and rarely stays contained to a workstation: an

Cloned voices and synthetic video calls now show up in payment fraud, help desk resets, and access requests. A structured drill shows security teams exactly where verification breaks down, while nothing is at stake. The call comes in at 4:40 on a Friday. It's the CFO, or it sounds exactly like her, asking the controller to push a supplier payment out before the weekend because a deal closes

The bug was simple: the login let you choose where your sign-in code went. Proving it on a live website was the hard part. TL;DR During an authorized assessment, an AI pentesting agent found a critical authentication bypass on a major insurer's production website, on its own. One login request let the user choose where the one-time passcode went. The attack was the easy part. Proving it

The dangerous part of a stolen identity isn't always the initial login. It's what an attacker can do with that access next. With AI accelerating reconnaissance, exploitation of vulnerabilities, and privilege escalation, attackers can move through systems faster than security teams can investigate. A compromised account that looks legitimate at first could become the starting point for a much

A user opens a browser tab, pastes data into a chat assistant, and nothing is installed. A developer adds a coding assistant as an editor extension, and nothing shows up in the installed programs list. Someone downloads a local model runtime, a program that runs models directly on the machine instead of sending data to an external service, because they were told not to send company data

The personal AI agents rollout is going about as smoothly as expected: Amazon now blocks Meta's Muse because it doesn't identify itself as a bot while browsing; Instinct keeps raising privacy concerns; and the horror stories from pretty much every personal AI agent keep piling up. Regardless, the tech world is mesmerized by the capabilities and potential of these services. Less than a year

In environments studied for the 2026 State of Agent Security Report, roughly 1,280 third-party products now embed AI. About 282 of them sit behind single sign-on. The other thousand are invisible to identity infrastructure by default, not because anyone hid them, but because an identity stack can only govern what authenticates through it, and most agents never do. That gap is the clearest

As enterprises race to deploy autonomous AI agents to accelerate business, a new report reveals they are tethered to security architectures built for a different era. The "Horizons of Identity Security" report from SailPoint highlights a critical “velocity paradox,” in which organizations invest in AI-speed business operations while continuing to rely on human-speed security controls, creating a

Phishing kits are no longer limited to copying a familiar login page and waiting for a victim to enter credentials. Attackers are increasingly building filtering, session management, and traffic controls into the infrastructure that delivers the phishing page itself. ANY.RUN has identified Wazza, a new phishkit targeting banking, manufacturing, and government organizations across the US, Europe

The 2026 findings are not just a year-over-year shift. They mark the latest point in a five-year arc where resilience, AI governance, human risk, and board scrutiny are converging inside the systems where work actually happens. For years, the enterprise cybersecurity story has been told as a straight line of escalation: more attacks, more data loss, more pressure, and more urgency. That

If you’re evaluating an agentic pentesting solution right now, you’ve probably heard the same pitch more than once: point it at a target, and it discovers, validates, and exploits attack paths autonomously, the way a real attacker would. That promise is worth taking seriously. It’s also worth pressure testing, and three questions do the heavy lifting.  What can the assessment actually

In 2024, MCP (Model Context Protocol) set out to become the USB-C of AI: one standard for connecting models, agents, and IDEs to tools and data. The protocol delivered. Thousands of developers built servers, and enterprises plugged them into agent workflows. The ecosystem around it fell short. Earlier this year, our team at OX Security,  traced critical vulnerabilities in Anthropic's MCP