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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteYes, LeftoverLocals can expose data processed by another program—including parts of an AI response—on some vulnerable GPU configurations. The demonstrated attack requires code to run through the GPU’s programming interface on the affected system. It is a local GPU-code attack, not a way for a remote visitor to extract data from any AI service simply by connecting to it.
What LeftoverLocals is—and what it is not
LeftoverLocals is a GPU memory-isolation vulnerability associated with CVE-2023-4969. A GPU kernel is a program submitted to the GPU. During computation, a kernel may use local memory, a software-managed, cache-like region. The researchers found cases in which data from one kernel remained available after that kernel finished and could be read by a later kernel running in a different process.
The security failure is inadequate isolation of residual GPU local memory. It is not evidence that every GPU leaks data, that every AI model is exposed, or that all data on a device can be read. Exposure depends on the GPU implementation, software stack, workload, and information left in local memory.
Can LeftoverLocals expose AI or LLM responses?
Trail of Bits demonstrated a proof of concept that recovered portions of interactive large language model (LLM) responses from GPU local memory. This shows that response content can be at risk on a vulnerable configuration; it does not establish that every response, prompt, or model parameter can be reconstructed in every deployment.
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The researchers’ finding matters beyond LLMs because other GPU workloads may also use local memory. In a CERT/CC vulnerability note, Trail of Bits researcher Tyler Sorensen said the researchers believed that many machine-learning implementations could be affected because common deep-neural-network operations, including matrix multiplication and convolutions, make heavy use of local memory. That is the researchers’ assessment of possible scope, not a measured count of affected systems.
Does the attack work remotely?
The demonstrated threat model is local: an attacker must be able to run a malicious GPU kernel, or equivalent code through the GPU’s programmable interface, on a system using an affected configuration. If separate users’ or processes’ GPU work runs on the same device, residual local-memory data may cross an intended process or container boundary.
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A network connection to an AI service, by itself, is not the demonstrated prerequisite. The findings do not justify describing LeftoverLocals as unauthenticated remote exploitation of any public AI endpoint. Whether an attacker can reach the necessary GPU interface depends on how the host, driver, runtime, permissions, and workload environment are configured.
Which GPUs were observed, and what does that list mean?
Trail of Bits and CERT/CC reported observations on selected AMD, Apple, and Qualcomm GPU platforms using interfaces including Metal, Vulkan, and OpenCL. Examples in the researchers’ tested configurations include:
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These are examples tested in 2023-era software and device configurations, reported in work published in 2024. They are not a complete inventory of affected products or a current patch-status list. A model not named in the test results cannot be assumed vulnerable or safe based on that omission. CERT/CC also reported that its testing did not observe the behavior on NVIDIA devices; that is a description of the testing performed, not a guarantee for every NVIDIA product or later software configuration.
How to check and reduce your exposure
There is no single universal patch instruction for every GPU. Check the current guidance from the GPU or device maker for the exact hardware and software combination rather than relying on a device name alone.
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- Identify the configuration. Record the exact GPU or system-on-chip, operating system, GPU driver or firmware, and GPU programming interface or runtime in use.
- Map where GPU work is shared. Determine whether users, processes, containers, or virtual machines can submit work to the same GPU. Shared access makes process-isolation guidance especially relevant.
- Check the vendor’s current security guidance. For AMD, consult the latest product and deployment tables in bulletin AMD-SB-6010, “GPU Memory Leaks,” associated with CVE-2023-4969. For Apple and Qualcomm devices, install current operating-system or device-maker updates and consult the applicable official security information. The available reporting does not establish a complete current, model-by-model patch matrix for those vendors.
- Apply only guidance that matches the product and deployment. Do not infer that a mitigation applies to a GPU, driver, firmware, or hosting arrangement merely because a similar model appears in a research test.
- Validate operational impact. If the vendor offers a mitigation that changes GPU concurrency, test it against the actual workload and isolation requirements before broad deployment.
What AMD’s mitigation changes
AMD’s AMD-SB-6010 bulletin describes a mode for supported products that prevents GPU processes from running in parallel and clears registers between processes. AMD says the mode is not enabled by default and must be set by an administrator. Its bulletin separates product and deployment guidance, so administrators should use the current table for the exact configuration.
Serializing workloads that otherwise ran concurrently can reduce performance; AMD also notes a lesser impact from register clearing. The trade-off is therefore between the mitigation’s isolation behavior and the throughput or concurrency the deployment needs. The bulletin’s supported products and instructions may change, so verify its current guidance before making a configuration change.
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What users and administrators should take away
LeftoverLocals is a real GPU isolation issue with a demonstrated path to recovering portions of LLM output, but its practical risk depends on access to GPU code execution and a vulnerable software-and-hardware configuration. For an individual device, keep its operating system and vendor software current. For a shared GPU environment, inventory the full stack and follow configuration-specific vendor guidance; there is no evidence-supported universal hardware purchase that fixes the issue.
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