How an AI Assistant Spilled Its Own Secrets, and Yours

Muhammad Adil — Tue, 18 Aug 2026 16:54:22 +0000

AI assistants are supposed to keep your data safe, not hand it over on demand. Yet a team of security researchers recently proved that Microsoft 365 Copilot could be manipulated into doing exactly that, without the user lifting a finger. The exploit didn’t require reverse engineering or deep technical tricks. Instead, the researchers simply asked Copilot how to bypass its own protections.

The Weakness in the System

Most AI tools, including Copilot, have guardrails to prevent unauthorized actions. For example, if you try to get an assistant to read your emails or extract passwords, it should refuse unless you explicitly confirm the request. These safeguards are meant to stop malicious prompts from executing automatically. But guardrails are only as strong as their weakest point, and in this case, the weak point was an undocumented feature.

The researchers at Varonis started by probing Copilot with seemingly innocent questions. They asked why certain commands required user confirmation, what URL structures triggered auto-execution, and how the system handled pre-filled prompts. Each answer revealed a little more about the underlying architecture. Eventually, Copilot disclosed a hidden parameter, ?autorun=1, that completely bypassed the need for user approval.

A Single Click, a Full Breach

With the hidden parameter in hand, the researchers crafted a malicious URL. When a victim clicked it, the link opened Copilot in their authenticated session and silently executed a pre-loaded prompt. The prompt could instruct Copilot to search the victim’s inbox, extract sensitive data, and send it to an attacker-controlled server. All of this happened in the background, with no visible warning or interaction required.

The victim receives a phishing email or message containing the malicious link.
They click it, loading Copilot in their browser with the ?autorun=1 parameter active.
The embedded prompt runs automatically, accessing emails, files, or other connected apps.
Copilot processes the request and sends extracted data to the attacker’s server via a disguised URL.
The victim remains unaware, even if they close the Copilot tab immediately.

The data exfiltration was cleverly hidden. The stolen information was encoded in base64 and appended to a URL that Copilot automatically opened. To the victim, it might look like a harmless redirect. Meanwhile, the attacker received everything from email addresses to passwords, whatever the prompt was designed to extract.

Memory Poisoning: A Long-Term Threat

The researchers didn’t stop at one-click exploits. They also demonstrated how Copilot’s memory feature could be hijacked. AI assistants like Copilot store user preferences, instructions, and context to avoid repeating questions. By embedding malicious instructions in a webpage, the researchers tricked Copilot into saving attacker-defined rules in its permanent memory.

Once poisoned, Copilot would follow these hidden instructions in future sessions. An attacker could force the assistant to forward sensitive outputs, filter responses, or even execute commands when certain conditions were met. The corrupted memory persisted across password changes and device resets, making it nearly impossible for users to detect or remove without manual inspection.

Why This Matters for AI Security

This attack highlights a fundamental flaw in how AI security is often designed. Instead of building systems that inherently resist misuse, developers rely on reactive guardrails, rules added after vulnerabilities are discovered. These guardrails can be bypassed, as seen here, or even turned against the system itself.

Copilot eventually disclosed undocumented parameters. I took those parameters and used them for prompts that ran automatically.

Microsoft patched the specific vulnerability in February by disabling the ?q= parameter’s ability to inject text. But the incident raises broader questions. How many other undocumented features exist in AI tools? How many more exploits are waiting to be discovered through simple conversation?

How to Protect Yourself

Treat AI assistant links like any other untrusted URL. If you didn’t expect it, don’t click it.
Monitor Copilot’s responses for unexpected behavior, such as opening unfamiliar tabs or summarizing sensitive data without prompting.
Limit the apps and services connected to your AI assistant. Fewer integrations mean fewer attack surfaces.
Regularly review your assistant’s memory settings. If something looks off, clear it and reconfigure your preferences.

This isn’t the first time AI assistants have been exploited with minimal effort, and it won’t be the last. The lesson is clear: convenience and security are often at odds. Until AI systems are built with proactive safeguards, users will need to stay vigilant, or risk handing over their data to the wrong hands.

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How Linux 7.3 Handles VRAM Starvation Without Slowing Down

Muhammad Adil — Tue, 18 Aug 2026 15:52:52 +0000

Running out of video memory used to mean stuttering, hangs, or even a full system freeze. Linux 7.3 changes that with a set of kernel-level tweaks that keep your desktop smooth even when VRAM is maxed out. The improvements target both gaming and professional workloads, but they don't work like magic. You need to understand the trade-offs and how to verify the behavior on your own hardware.

What Happens When VRAM Runs Out

Older kernels treated VRAM exhaustion as an all-or-nothing problem. When the GPU driver couldn't allocate more memory, it would either fall back to system RAM (slow) or fail outright (crash). Linux 7.3 introduces a more nuanced approach. The kernel now tracks VRAM usage in smaller chunks and prioritizes critical allocations. Non-essential buffers get demoted to system memory earlier, freeing up space for what matters most.

The Key Changes in 7.3

Dynamic buffer demotion: The kernel can now move less important GPU buffers to system RAM without waiting for a full allocation failure.
Priority-based eviction: Critical rendering tasks get first dibs on remaining VRAM, while background processes take the hit.
Transparent fallback: When buffers are moved to system RAM, the GPU driver handles the transition without stalling the pipeline.
Better memory accounting: The kernel now tracks VRAM usage more accurately, preventing false positives that used to trigger unnecessary fallbacks.

Who Benefits the Most

These changes matter most for two groups. Gamers running older GPUs with limited VRAM will see fewer frame drops when pushing settings too high. Professional users working with large textures or complex 3D scenes can keep working without sudden freezes. The improvements are less noticeable on high-end GPUs with plenty of VRAM, but even there, the new memory accounting helps prevent rare edge cases.

How to Test It Yourself

You don't need to wait for a benchmark to see the difference. A simple test involves running a game or application that maxes out your VRAM while monitoring system behavior. Start with a tool like glxinfo to check your current VRAM usage. Then launch a demanding workload and watch how the system responds when VRAM fills up. The key metric isn't just frame rate but how smoothly the system recovers when memory pressure eases.

Use MANGOHUD or vkmark to monitor VRAM usage in real time.
Run a game or 3D application that pushes your GPU to its limits.
Watch for stutters or freezes when VRAM usage hits 100%.
Compare the behavior before and after upgrading to Linux 7.3.

The Trade-Offs You Should Know

The new system isn't perfect. Moving buffers to system RAM introduces latency, so you might see a slight performance dip when VRAM is exhausted. The kernel also has to make judgment calls about which buffers to demote, and it doesn't always get it right. In some cases, you might still need to tweak application settings to avoid hitting VRAM limits in the first place.

What's Next for VRAM Management

Linux 7.3 is just the first step. Future kernels will likely refine the eviction policies and improve the coordination between GPU drivers and the memory subsystem. There's also ongoing work to make the fallback to system RAM more efficient, reducing the performance penalty when VRAM runs out. For now, the changes in 7.3 make Linux a more reliable platform for graphics-heavy workloads, even on older hardware.

If you're running a system with limited VRAM, upgrading to Linux 7.3 is worth the effort. The improvements won't make your GPU faster, but they will keep your system usable when memory gets tight. Test it with your own workloads and see the difference for yourself.