CVE-2025-34091 : Détail

CVE-2025-34091

8.8
/
Haute
A04-Insecure Design
Local
2025-07-02
19h25 +00:00
2025-07-03
03h55 +00:00
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Descriptions du CVE

Chrome Cookie Encryption Bypass via Padding Oracle Attack on AppBound Encryption

A padding oracle vulnerability exists in Google Chrome’s AppBound cookie encryption mechanism due to observable decryption failure behavior in Windows Event Logs when handling malformed ciphertext in SYSTEM-DPAPI-encrypted blobs. A local attacker can repeatedly send malformed ciphertexts to the Chrome elevation service and distinguish between padding and MAC errors, enabling a padding oracle attack. This allows partial decryption of the SYSTEM-DPAPI layer and eventual recovery of the user-DPAPI encrypted cookie key, which is trivially decrypted by the attacker’s own context. This issue undermines the core purpose of AppBound Encryption by enabling low-privileged cookie theft through cryptographic misuse and verbose error feedback. Confirmed in Google Chrome with AppBound Encryption enabled. Other Chromium-based browsers may be affected if they implement similar COM-based encryption mechanisms. This behavior arises from a combination of Chrome’s AppBound implementation and the way Microsoft Windows DPAPI reports decryption failures via Event Logs. As such, the vulnerability relies on cryptographic behavior and error visibility in all supported versions of Windows.

Informations du CVE

Faiblesses connexes

CWE-ID Nom de la faiblesse Source
CWE-203 Observable Discrepancy
The product behaves differently or sends different responses under different circumstances in a way that is observable to an unauthorized actor, which exposes security-relevant information about the state of the product, such as whether a particular operation was successful or not.
CWE-209 Generation of Error Message Containing Sensitive Information
The product generates an error message that includes sensitive information about its environment, users, or associated data.

Métriques

Métriques Score Gravité CVSS Vecteur Source
V4.0 8.8 HIGH CVSS:4.0/AV:L/AC:H/AT:P/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

Base: Exploitabilty Metrics

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

Attack Vector

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

Local

The vulnerable system is not bound to the network stack and the attacker’s path is via read/write/execute capabilities. Either: the attacker exploits the vulnerability by accessing the target system locally (e.g., keyboard, console), or through terminal emulation (e.g., SSH); or the attacker relies on User Interaction by another person to perform actions required to exploit the vulnerability (e.g., using social engineering techniques to trick a legitimate user into opening a malicious document).

Attack Complexity

This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit.

High

The successful attack depends on the evasion or circumvention of security-enhancing techniques in place that would otherwise hinder the attack. These include: Evasion of exploit mitigation techniques. The attacker must have additional methods available to bypass security measures in place. For example, circumvention of address space randomization (ASLR) or data execution prevention (DEP) must be performed for the attack to be successful. Obtaining target-specific secrets. The attacker must gather some target-specific secret before the attack can be successful. A secret is any piece of information that cannot be obtained through any amount of reconnaissance. To obtain the secret the attacker must perform additional attacks or break otherwise secure measures (e.g. knowledge of a secret key may be needed to break a crypto channel). This operation must be performed for each attacked target.

Attack Requirements

This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack.

Present

The successful attack depends on the presence of specific deployment and execution conditions of the vulnerable system that enable the attack. These include: A race condition must be won to successfully exploit the vulnerability. The successfulness of the attack is conditioned on execution conditions that are not under full control of the attacker. The attack may need to be launched multiple times against a single target before being successful. Network injection. The attacker must inject themselves into the logical network path between the target and the resource requested by the victim (e.g. vulnerabilities requiring an on-path attacker).

Privileges Required

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

Low

The attacker requires privileges that provide basic capabilities that are typically limited to settings and resources owned by a single low-privileged 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 system.

None

The vulnerable system can be exploited without interaction from any human user, other than the attacker. Examples include: a remote attacker is able to send packets to a target system a locally authenticated attacker executes code to elevate privileges

Base: Impact Metrics

The Impact metrics capture the effects of a successfully exploited vulnerability. 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 managed by the system due to a successfully exploited vulnerability.

High

There is a total loss of confidentiality, resulting in all information within the Vulnerable System being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact. For example, an attacker steals the administrator's password, or private encryption keys of a web server.

Integrity Impact

This metric measures the impact to integrity of a successfully exploited vulnerability.

High

There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the Vulnerable System. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the Vulnerable System.

Availability Impact

This metric measures the impact to the availability of the impacted system 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 Vulnerable System; 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 Vulnerable System (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).

Sub Confidentiality Impact

High

There is a total loss of confidentiality, resulting in all resources within the Subsequent System being divulged to the attacker. Alternatively, access to only some restricted information is obtained, but the disclosed information presents a direct, serious impact. For example, an attacker steals the administrator's password, or private encryption keys of a web server.

Sub Integrity Impact

High

There is a total loss of integrity, or a complete loss of protection. For example, the attacker is able to modify any/all files protected by the Subsequent System. Alternatively, only some files can be modified, but malicious modification would present a direct, serious consequence to the Subsequent System.

Sub Availability Impact

High

There is a total loss of availability, resulting in the attacker being able to fully deny access to resources in the Subsequent System; 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 Subsequent System (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).

Threat Metrics

The Threat metrics measure the current state of exploit techniques or code availability for a vulnerability.

Environmental Metrics

These metrics enable the consumer analyst to customize the resulting score depending on the importance of the affected IT asset to a user’s organization, measured in terms of complementary/alternative security controls in place, Confidentiality, Integrity, and Availability. The metrics are the modified equivalent of Base metrics and are assigned values based on the system placement within organizational infrastructure.

Supplemental Metrics

Supplemental metric group provides new metrics that describe and measure additional extrinsic attributes of a vulnerability. While the assessment of Supplemental metrics is provisioned by the provider, the usage and response plan of each metric within the Supplemental metric group is determined by the consumer.

EPSS

EPSS est un modèle de notation qui prédit la probabilité qu'une vulnérabilité soit exploitée.

Score EPSS

Le modèle EPSS produit un score de probabilité compris entre 0 et 1 (0 et 100 %). Plus la note est élevée, plus la probabilité qu'une vulnérabilité soit exploitée est grande.

Percentile EPSS

Le percentile est utilisé pour classer les CVE en fonction de leur score EPSS. Par exemple, une CVE dans le 95e percentile selon son score EPSS est plus susceptible d'être exploitée que 95 % des autres CVE. Ainsi, le percentile sert à comparer le score EPSS d'une CVE par rapport à d'autres CVE.

Références