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nebula-mesh's stores enrollment tokens unhashed in SQLite

Moderate severity GitHub Reviewed Published May 21, 2026 in forgekeep/nebula-mesh

Package

gomod github.com/juev/nebula-mesh (Go)

Affected versions

<= 0.3.1

Patched versions

0.3.2

Description

internal/store/sqlite.go:1177,1192,1221,1245 — the enrollment_tokens.token column holds the raw UUID token. ConsumeToken does WHERE token = ? against the raw string. Compare with operator_api_keys.key_hash, which is SHA-256 hex (constructed in internal/api/middleware.go:51-53).

Affected

All released versions up to v0.3.0.

Threat model

Read access to nebula-mgmt.db: backup, snapshot, file-system access, future SQL-injection sink. The principle of defense-in-depth: API keys are hashed at rest; enrollment tokens — which grant the same lifecycle authority over a host's identity — are not.

An attacker who reads the DB before a legitimate agent enrolls can consume the single-use token first, mint a cert against their own keypair, and take the agent's intended Nebula identity.

Suggested fix

  1. Schema migration: rename enrollment_tokens.token → token_hash (or add the new column and drop the old after backfill of pending rows).
  2. Store SHA-256 of token on create: 
    sum := sha256.Sum256([]byte(token))
row.TokenHash = hex.EncodeToString(sum[:])
  3. ConsumeToken accepts the raw token, hashes once, looks up by hash, atomically marks consumed.

Side bonus: take this opportunity to switch the token format from uuid.New().String() (122 bits) to hex.EncodeToString(crypto/rand 32 bytes) (256 bits), matching the project's session-token and API-key conventions. UUIDs are recognisable in logs and crash dumps; opaque hex blends in.

TOTP recovery codes appear to already be SHA-256 hashed at rest (internal/web/totp.go:74-78) — confirming that pattern is intentional elsewhere, just missed here.

References

@juev juev published to forgekeep/nebula-mesh May 21, 2026
Published to the GitHub Advisory Database Jun 22, 2026
Reviewed Jun 22, 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 Low
Attack Requirements None
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality Low
Integrity High
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:L/AT:N/PR:L/UI:N/VC:L/VI:H/VA:N/SC:N/SI:N/SA:N/E:U

EPSS score

Weaknesses

Cleartext Storage of Sensitive Information

The product stores sensitive information in cleartext within a resource that might be accessible to another control sphere. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-ghmh-jhmj-wcmf

Source code

Credits

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