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Tesla has decompression bomb on response body

High severity GitHub Reviewed Published Jun 2, 2026 in elixir-tesla/tesla • Updated Jul 10, 2026

Package

erlang tesla (Erlang)

Affected versions

>= 0.6.0, < 1.18.3

Patched versions

1.18.3

Description

Summary

Any Tesla client pipeline that includes Tesla.Middleware.DecompressResponse or Tesla.Middleware.Compression eagerly decompresses HTTP response bodies with no size limit. A server under attacker control (or reached via a redirect) can return a tiny gzip-encoded payload that expands into gigabytes of BEAM heap, crashing or freezing the calling process. Stacking multiple content-encoding tokens multiplies the amplification exponentially.

Details

decompress_body/2 in lib/tesla/middleware/compression.ex passes the full response body to :zlib.gunzip/1 or :zlib.unzip/1 with no cap on output size. The list of codec tokens comes from splitting the content-encoding header on commas, and decompress_body/2 recurses once per token. A response advertising content-encoding: gzip, gzip, gzip, gzip triggers four recursive decompression passes. Each gzip layer can expand its input roughly 1000x, so a 284-byte wire payload with four layers inflates to approximately 1 GB at the innermost pass, all materialised as a single binary in the caller's heap.

PoC

  1. Serve an HTTP response with content-encoding: gzip, gzip, gzip, gzip where the body is a 1 GB block of zeros compressed through four successive gzip passes.
  2. Send that response to a Tesla client whose pipeline includes Tesla.Middleware.DecompressResponse.
  3. decompress_body/2 recurses four times without any size check, materialising ~1 GB in the calling process's heap.
  4. Repeated or sufficiently large requests exhaust available memory and crash or freeze the node.

Impact

High severity (CVSS v4.0: 8.2). Any application using tesla 0.6.0 through 1.18.2 with Tesla.Middleware.DecompressResponse or Tesla.Middleware.Compression in its pipeline is vulnerable. The attacker only needs to control a server the client contacts, including via redirects. Fixed in tesla 1.18.3.

Configurations

The application must include Tesla.Middleware.DecompressResponse or Tesla.Middleware.Compression in its Tesla middleware pipeline.

Resources

References

@yordis yordis published to elixir-tesla/tesla Jun 2, 2026
Published by the National Vulnerability Database Jun 2, 2026
Published to the GitHub Advisory Database Jul 10, 2026
Reviewed Jul 10, 2026
Last updated Jul 10, 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 Present
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:P/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.
(25th percentile)

Weaknesses

Improper Handling of Highly Compressed Data (Data Amplification)

The product does not handle or incorrectly handles a compressed input with a very high compression ratio that produces a large output. Learn more on MITRE.

CVE ID

CVE-2026-48594

GHSA ID

GHSA-mc85-72gr-vm9f

Source code

Credits

Dependabot alerts are not supported on some or all of the ecosystems on this advisory.

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