CVE-2024-50099 Detail

CVE-2024-50099

Score / Severity

5.5

Medium

EPSS

0.04%V3

Vector

Local

Published

2024-11-05
17h07 +00:00

Modified

2024-12-19
09h33 +00:00

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CVE Descriptions

arm64: probes: Remove broken LDR (literal) uprobe support

In the Linux kernel, the following vulnerability has been resolved: arm64: probes: Remove broken LDR (literal) uprobe support The simulate_ldr_literal() and simulate_ldrsw_literal() functions are unsafe to use for uprobes. Both functions were originally written for use with kprobes, and access memory with plain C accesses. When uprobes was added, these were reused unmodified even though they cannot safely access user memory. There are three key problems: 1) The plain C accesses do not have corresponding extable entries, and thus if they encounter a fault the kernel will treat these as unintentional accesses to user memory, resulting in a BUG() which will kill the kernel thread, and likely lead to further issues (e.g. lockup or panic()). 2) The plain C accesses are subject to HW PAN and SW PAN, and so when either is in use, any attempt to simulate an access to user memory will fault. Thus neither simulate_ldr_literal() nor simulate_ldrsw_literal() can do anything useful when simulating a user instruction on any system with HW PAN or SW PAN. 3) The plain C accesses are privileged, as they run in kernel context, and in practice can access a small range of kernel virtual addresses. The instructions they simulate have a range of +/-1MiB, and since the simulated instructions must itself be a user instructions in the TTBR0 address range, these can address the final 1MiB of the TTBR1 acddress range by wrapping downwards from an address in the first 1MiB of the TTBR0 address range. In contemporary kernels the last 8MiB of TTBR1 address range is reserved, and accesses to this will always fault, meaning this is no worse than (1). Historically, it was theoretically possible for the linear map or vmemmap to spill into the final 8MiB of the TTBR1 address range, but in practice this is extremely unlikely to occur as this would require either: * Having enough physical memory to fill the entire linear map all the way to the final 1MiB of the TTBR1 address range. * Getting unlucky with KASLR randomization of the linear map such that the populated region happens to overlap with the last 1MiB of the TTBR address range. ... and in either case if we were to spill into the final page there would be larger problems as the final page would alias with error pointers. Practically speaking, (1) and (2) are the big issues. Given there have been no reports of problems since the broken code was introduced, it appears that no-one is relying on probing these instructions with uprobes. Avoid these issues by not allowing uprobes on LDR (literal) and LDRSW (literal), limiting the use of simulate_ldr_literal() and simulate_ldrsw_literal() to kprobes. Attempts to place uprobes on LDR (literal) and LDRSW (literal) will be rejected as arm_probe_decode_insn() will return INSN_REJECTED. In future we can consider introducing working uprobes support for these instructions, but this will require more significant work.

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 More informations 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.	[email protected]

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.

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