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swift-nio-http2: Missing CR/LF/NUL validation in header values

Moderate severity GitHub Reviewed Published Jul 23, 2026 in apple/swift-nio-http2 • Updated Jul 24, 2026

Package

swift swift-nio-http2 (Swift)

Affected versions

< 1.45.0

Patched versions

1.45.0

Description

Summary

SwiftNIO HTTP/2 was missing validation on inbound HEADERS frames that let
CR, LF, NUL, SP and other control characters reach an HTTP/1.1 backend
through NIOHTTP2's HTTP/2-to-HTTP/1 codec, enabling HTTP request smuggling
or response splitting.

Impact

Two related gaps in inbound header validation, against any application
using HTTP2ToHTTP1Codec (or HTTP2FramePayloadToHTTP1Codec) to front an
HTTP/1.1 backend:

Regular header field values were only checked against a forbidden-name
list (connection, transfer-encoding, proxy-connection, keep-alive,
upgrade); the value itself was never inspected. An attacker-controlled
regular header field value containing CR or LF passed validation and, once
serialized as name: value CRLF by the codec, terminated the field early
and injected extra header lines into the outbound HTTP/1.1 message.

Pseudo-header values (:path in particular) were only checked against
CR, LF and NUL. A :path value containing SP serializes into the
request-target of METHOD SP request-target SP HTTP-version CRLF, so a
value like /a HTTP/1.1 produces GET /a HTTP/1.1 HTTP/1.1 — a
parser-differential request line depending on whether a downstream reader
takes the first or last SP-delimited token as the version.

Neither of these is reachable on a stock pipeline: NIOHTTP1's outbound
validator (enableOutboundHeaderValidation, on by default) already rejects
these characters on write. The exposure is pipelines that skip outbound
validation, or any code that reads validated-looking HTTPRequestHead.headers
and forwards the values on trusting that HTTP/2 already checked them.

Fix

Fixed in 48bfd90 and 45bdf67.

Mitigation

Upgrade to 1.45.0

References

@glbrntt glbrntt published to apple/swift-nio-http2 Jul 23, 2026
Published by the National Vulnerability Database Jul 23, 2026
Published to the GitHub Advisory Database Jul 24, 2026
Reviewed Jul 24, 2026
Last updated Jul 24, 2026

Severity

Moderate

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

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:H/AT:P/PR:N/UI:N/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(8th percentile)

Weaknesses

Improper Neutralization of CRLF Sequences in HTTP Headers ('HTTP Request/Response Splitting')

The product receives data from an HTTP agent/component (e.g., web server, proxy, browser, etc.), but it does not neutralize or incorrectly neutralizes CR and LF characters before the data is included in outgoing HTTP headers. Learn more on MITRE.

Inconsistent Interpretation of HTTP Requests ('HTTP Request/Response Smuggling')

The product acts as an intermediary HTTP agent (such as a proxy or firewall) in the data flow between two entities such as a client and server, but it does not interpret malformed HTTP requests or responses in ways that are consistent with how the messages will be processed by those entities that are at the ultimate destination. Learn more on MITRE.

CVE ID

CVE-2026-64785

GHSA ID

GHSA-q3g2-m552-3r9c

Source code

Credits

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