CVE-2021-47275 : Detail

CVE-2021-47275

5.5
/
Medium
0.06%V4
Local
2024-05-21
14h20 +00:00
2024-12-19
07h38 +00:00
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CVE Descriptions

bcache: avoid oversized read request in cache missing code path

In the Linux kernel, the following vulnerability has been resolved: bcache: avoid oversized read request in cache missing code path In the cache missing code path of cached device, if a proper location from the internal B+ tree is matched for a cache miss range, function cached_dev_cache_miss() will be called in cache_lookup_fn() in the following code block, [code block 1] 526 unsigned int sectors = KEY_INODE(k) == s->iop.inode 527 ? min_t(uint64_t, INT_MAX, 528 KEY_START(k) - bio->bi_iter.bi_sector) 529 : INT_MAX; 530 int ret = s->d->cache_miss(b, s, bio, sectors); Here s->d->cache_miss() is the call backfunction pointer initialized as cached_dev_cache_miss(), the last parameter 'sectors' is an important hint to calculate the size of read request to backing device of the missing cache data. Current calculation in above code block may generate oversized value of 'sectors', which consequently may trigger 2 different potential kernel panics by BUG() or BUG_ON() as listed below, 1) BUG_ON() inside bch_btree_insert_key(), [code block 2] 886 BUG_ON(b->ops->is_extents && !KEY_SIZE(k)); 2) BUG() inside biovec_slab(), [code block 3] 51 default: 52 BUG(); 53 return NULL; All the above panics are original from cached_dev_cache_miss() by the oversized parameter 'sectors'. Inside cached_dev_cache_miss(), parameter 'sectors' is used to calculate the size of data read from backing device for the cache missing. This size is stored in s->insert_bio_sectors by the following lines of code, [code block 4] 909 s->insert_bio_sectors = min(sectors, bio_sectors(bio) + reada); Then the actual key inserting to the internal B+ tree is generated and stored in s->iop.replace_key by the following lines of code, [code block 5] 911 s->iop.replace_key = KEY(s->iop.inode, 912 bio->bi_iter.bi_sector + s->insert_bio_sectors, 913 s->insert_bio_sectors); The oversized parameter 'sectors' may trigger panic 1) by BUG_ON() from the above code block. And the bio sending to backing device for the missing data is allocated with hint from s->insert_bio_sectors by the following lines of code, [code block 6] 926 cache_bio = bio_alloc_bioset(GFP_NOWAIT, 927 DIV_ROUND_UP(s->insert_bio_sectors, PAGE_SECTORS), 928 &dc->disk.bio_split); The oversized parameter 'sectors' may trigger panic 2) by BUG() from the agove code block. Now let me explain how the panics happen with the oversized 'sectors'. In code block 5, replace_key is generated by macro KEY(). From the definition of macro KEY(), [code block 7] 71 #define KEY(inode, offset, size) \ 72 ((struct bkey) { \ 73 .high = (1ULL << 63) | ((__u64) (size) << 20) | (inode), \ 74 .low = (offset) \ 75 }) Here 'size' is 16bits width embedded in 64bits member 'high' of struct bkey. But in code block 1, if "KEY_START(k) - bio->bi_iter.bi_sector" is very probably to be larger than (1<<16) - 1, which makes the bkey size calculation in code block 5 is overflowed. In one bug report the value of parameter 'sectors' is 131072 (= 1 << 17), the overflowed 'sectors' results the overflowed s->insert_bio_sectors in code block 4, then makes size field of s->iop.replace_key to be 0 in code block 5. Then the 0- sized s->iop.replace_key is inserted into the internal B+ tree as cache missing check key (a special key to detect and avoid a racing between normal write request and cache missing read request) as, [code block 8] 915 ret = bch_btree_insert_check_key(b, &s->op, &s->iop.replace_key); Then the 0-sized s->iop.replace_key as 3rd parameter triggers the bkey size check BUG_ON() in code block 2, and causes the kernel panic 1). Another ke ---truncated---

CVE Informations

Related Weaknesses

CWE-ID Weakness Name Source
CWE Other No informations.

Metrics

Metrics Score Severity CVSS Vector Source
V3.1 5.5 MEDIUM CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

Base: Exploitabilty Metrics

The Exploitability metrics reflect the characteristics of the thing that is vulnerable, which we refer to formally as the vulnerable component.

Attack Vector

This metric reflects the context by which vulnerability exploitation is possible.

Local

The vulnerable component is not bound to the network stack and the attacker’s path is via read/write/execute capabilities.

Attack Complexity

This metric describes the conditions beyond the attacker’s control that must exist in order to exploit the vulnerability.

Low

Specialized access conditions or extenuating circumstances do not exist. An attacker can expect repeatable success when attacking the vulnerable component.

Privileges Required

This metric describes the level of privileges an attacker must possess before successfully exploiting the vulnerability.

Low

The attacker requires privileges that provide basic user capabilities that could normally affect only settings and files owned by a user. Alternatively, an attacker with Low privileges has the ability to access only non-sensitive resources.

User Interaction

This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable component.

None

The vulnerable system can be exploited without interaction from any user.

Base: Scope Metrics

The Scope metric captures whether a vulnerability in one vulnerable component impacts resources in components beyond its security scope.

Scope

Formally, a security authority is a mechanism (e.g., an application, an operating system, firmware, a sandbox environment) that defines and enforces access control in terms of how certain subjects/actors (e.g., human users, processes) can access certain restricted objects/resources (e.g., files, CPU, memory) in a controlled manner. All the subjects and objects under the jurisdiction of a single security authority are considered to be under one security scope. If a vulnerability in a vulnerable component can affect a component which is in a different security scope than the vulnerable component, a Scope change occurs. Intuitively, whenever the impact of a vulnerability breaches a security/trust boundary and impacts components outside the security scope in which vulnerable component resides, a Scope change occurs.

Unchanged

An exploited vulnerability can only affect resources managed by the same security authority. In this case, the vulnerable component and the impacted component are either the same, or both are managed by the same security authority.

Base: Impact Metrics

The Impact metrics capture the effects of a successfully exploited vulnerability on the component that suffers the worst outcome that is most directly and predictably associated with the attack. Analysts should constrain impacts to a reasonable, final outcome which they are confident an attacker is able to achieve.

Confidentiality Impact

This metric measures the impact to the confidentiality of the information resources managed by a software component due to a successfully exploited vulnerability.

None

There is no loss of confidentiality within the impacted component.

Integrity Impact

This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information.

None

There is no loss of integrity within the impacted component.

Availability Impact

This metric measures the impact to the availability of the impacted component resulting from a successfully exploited vulnerability.

High

There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the impacted component; this loss is either sustained (while the attacker continues to deliver the attack) or persistent (the condition persists even after the attack has completed). Alternatively, the attacker has the ability to deny some availability, but the loss of availability presents a direct, serious consequence to the impacted component (e.g., the attacker cannot disrupt existing connections, but can prevent new connections; the attacker can repeatedly exploit a vulnerability that, in each instance of a successful attack, leaks a only small amount of memory, but after repeated exploitation causes a service to become completely unavailable).

Temporal Metrics

The Temporal metrics measure the current state of exploit techniques or code availability, the existence of any patches or workarounds, or the confidence in the description of a vulnerability.

Environmental Metrics

These metrics enable the analyst to customize the CVSS score depending on the importance of the affected IT asset to a user’s organization, measured in terms of Confidentiality, Integrity, and Availability.

nvd@nist.gov

EPSS

EPSS is a scoring model that predicts the likelihood of a vulnerability being exploited.

EPSS Score

The EPSS model produces a probability score between 0 and 1 (0 and 100%). The higher the score, the greater the probability that a vulnerability will be exploited.

EPSS Percentile

The percentile is used to rank CVE according to their EPSS score. For example, a CVE in the 95th percentile according to its EPSS score is more likely to be exploited than 95% of other CVE. Thus, the percentile is used to compare the EPSS score of a CVE with that of other CVE.

Products Mentioned

Configuraton 0

Linux>>Linux_kernel >> Version To (excluding) 5.12.11

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

Linux>>Linux_kernel >> Version 5.13

References