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DNSJava DNSSEC Bypass

High severity GitHub Reviewed Published Jul 21, 2024 in dnsjava/dnsjava • Updated Sep 4, 2024

Package

maven dnsjava:dnsjava (Maven)

Affected versions

< 3.6.0

Patched versions

3.6.0

Description

Summary

Records in DNS replies are not checked for their relevance to the query, allowing an attacker to respond with RRs from different zones.

Details

DNS Messages are not authenticated. They do not guarantee that

  • received RRs are authentic
  • not received RRs do not exist
  • all or any received records in a response relate to the request

Applications utilizing DNSSEC generally expect these guarantees to be met, however DNSSEC by itself only guarantees the first two.
To meet the third guarantee, resolvers generally follow an (undocumented, as far as RFCs go) algorithm such as: (simplified, e.g. lacks DNSSEC validation!)

  1. denote by QNAME the name you are querying (e.g. fraunhofer.de.), and initialize a list of aliases
  2. if the ANSWER section contains a valid PTR RRSet for QNAME, return it (and optionally return the list of aliases as well)
  3. if the ANSWER section contains a valid CNAME RRSet for QNAME, add it to the list of aliases. Set QNAME to the CNAME's target and go to 2.
  4. Verify that QNAME does not have any PTR, CNAME and DNAME records using valid NSEC or NSEC3 records. Return null.

Note that this algorithm relies on NSEC records and thus requires a considerable portion of the DNSSEC specifications to be implemented. For this reason, it cannot be performed by a DNS client (aka application) and is typically performed as part of the resolver logic.

dnsjava does not implement a comparable algorithm, and the provided APIs instead return either

  • the received DNS message itself (e.g. when using a ValidatingResolver such as in this example), or
  • essentially just the contents of its ANSWER section (e.g. when using a LookupSession such as in this example)

If applications blindly filter the received results for RRs of the desired record type (as seems to be typical usage for dnsjava), a rogue recursive resolver or (on UDP/TCP connections) a network attacker can

  • In addition to the actual DNS response, add RRs irrelevant to the query but of the right datatype, e.g. from another zone, as long as that zone is correctly using DNSSEC, or
  • completely exchange the relevant response records

Impact

DNS(SEC) libraries are usually used as part of a larger security framework.
Therefore, the main misuses of this vulnerability concern application code, which might take the returned records as authentic answers to the request.
Here are three concrete examples of where this might be detrimental:

  • RFC 6186 specifies that to connect to an IMAP server for a user, a mail user agent should retrieve certain SRV records and send the user's credentials to the specified servers. Exchanging the SRV records can be a tool to redirect the credentials.
  • When delivering mail via SMTP, MX records determine where to deliver the mails to. Exchanging the MX records might lead to information disclosure. Additionally, an exchange of TLSA records might allow attackers to intercept TLS traffic.
  • Some research projects like LIGHTest are trying to manage CA trust stores via URI and SMIMEA records in the DNS. Exchanging these allows manipulating the root of trust for dependent applications.

Mitigations

At this point, the following mitigations are recommended:

  • When using a ValidatingResolver, ignore any Server indications of whether or not data was available (e.g. NXDOMAIN, NODATA, ...).
  • For APIs returning RRs from DNS responses, filter the RRs using an algorithm such as the one above. This includes e.g. LookupSession.lookupAsync.
  • Remove APIs dealing with raw DNS messages from the examples section or place a noticable warning above.

References

@ibauersachs ibauersachs published to dnsjava/dnsjava Jul 21, 2024
Published by the National Vulnerability Database Jul 22, 2024
Published to the GitHub Advisory Database Jul 22, 2024
Reviewed Jul 22, 2024
Last updated Sep 4, 2024

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements Present
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability None
Subsequent System Impact Metrics
Confidentiality High
Integrity High
Availability Low

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
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. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
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. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:N/SC:H/SI:H/SA:L

EPSS score

0.043%
(11th percentile)

Weaknesses

CVE ID

CVE-2024-25638

GHSA ID

GHSA-cfxw-4h78-h7fw

Source code

Credits

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