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CVE-2021-47275 is a vulnerability in the Linux kernel's bcache driver that was discovered and resolved in 2021. The issue occurs in the cache missing code path of the cached device, where the cached_dev_cache_miss() function can create an oversized read request when calculating the size of data read from the backing device (NVD, Red Hat).
The vulnerability stems from an issue in the bcache driver where the calculation for the size of data read from the backing device can produce an excessively large value. This occurs when the parameter 'sectors' passed to cached_dev_cache_miss() may cause overflow in the code, potentially leading to two different kernel panics triggered by BUG() or BUG_ON() statements. The issue can manifest when KEY_START(k) - bio->bi_iter.bi_sector is larger than (1<<16) - 1, causing bkey size calculation overflow (Kernel Git).
The vulnerability can lead to kernel crashes when specific conditions related to oversized read requests are met. This primarily affects system stability and availability, as the oversized parameter 'sectors' can trigger kernel panics through two different paths: one in the bch_btree_insert_key() function and another in the biovec_slab() function (Red Hat).
The vulnerability requires specific conditions related to oversized read requests in the bcache driver. While it can cause system instability, exploitation requires particular circumstances involving the cache missing code path. Red Hat has rated this as having moderate severity, indicating that while it poses a risk, the exploitation complexity is relatively high (Red Hat).
The issue has been fixed by implementing a size limit check in the bcache driver. The fix adds a local variable size_limit that is set to the minimum value between the max bkey size and max bio bvecs number. This ensures that s->insert_bio_sectors will never result in oversized replace_key size or bio bvecs number. Users are advised to update their systems with the patched version of the Linux kernel (Kernel Git).
Source: This report was generated using AI
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