PYSEC-2026-2952
Vulnerability from pysec - Published: 2026-07-13 15:02 - Updated: 2026-07-13 16:05
VLAI
Details
The fix for CVE-2026-40315 added input validation to SQLiteConversationStore only. Nine sibling backends — MySQL, PostgreSQL, async SQLite/MySQL/PostgreSQL, Turso, SingleStore, Supabase, SurrealDB — pass table_prefix straight into f-string SQL. Same root cause, same code pattern, same exploitation. 52 unvalidated injection points across the codebase.
postgres.py additionally accepts an unvalidated schema parameter used directly in DDL.
Severity
High — CWE-89 (SQL Injection)
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N — 8.1
Exploitable in any deployment where table_prefix is derived from external input (multi-tenant setups, API-driven configuration, user-modifiable config files). Default config ("praison_") is not affected.
Details
The CVE-2026-40315 fix added this guard to sqlite.py:52:
# sqlite.py — PATCHED
import re
if not re.match(r'^[a-zA-Z0-9_]*$', table_prefix):
raise ValueError("table_prefix must contain only alphanumeric characters and underscores")
The following backends perform the identical table_prefix → f-string SQL pattern without this guard:
| Backend | File | Line | Injection points |
|---|---|---|---|
| MySQL | persistence/conversation/mysql.py |
65 | 5 |
| PostgreSQL | persistence/conversation/postgres.py |
89 (+schema:88) | 10 |
| Async SQLite | persistence/conversation/async_sqlite.py |
43 | 13 |
| Async MySQL | persistence/conversation/async_mysql.py |
65 | 13 |
| Async PostgreSQL | persistence/conversation/async_postgres.py |
63 | 13 |
| Turso/LibSQL | persistence/conversation/turso.py |
66 | 9 |
| SingleStore | persistence/conversation/singlestore.py |
51 | 7 |
| Supabase | persistence/conversation/supabase.py |
68 | 9 |
| SurrealDB | persistence/conversation/surrealdb.py |
57 | 8 |
| Total | 9 backends | 52 injection points |
Additionally, praisonai-agents/praisonaiagents/storage/backends.py:179 (SQLiteBackend) accepts table_name without validation.
PoC
#!/usr/bin/env python3
"""
Demonstrates: sqlite.py rejects malicious table_prefix, mysql.py accepts it.
Run: python3 poc.py (no dependencies required)
"""
import re
payload = "x'; DROP TABLE users; --"
# ── SQLite (patched) ────────────────────────────────────────────────
try:
if not re.match(r'^[a-zA-Z0-9_]*$', payload):
raise ValueError("blocked")
print(f"[SQLite] FAIL — accepted: {payload}")
except ValueError:
print(f"[SQLite] OK — rejected malicious table_prefix")
# ── MySQL (unpatched) ───────────────────────────────────────────────
sessions_table = f"{payload}sessions"
sql = f"CREATE TABLE IF NOT EXISTS {sessions_table} (session_id VARCHAR(255) PRIMARY KEY)"
print(f"[MySQL] VULN — generated SQL:\n {sql}")
# ── PostgreSQL (unpatched — both table_prefix AND schema) ──────────
schema = "public; DROP SCHEMA data CASCADE; --"
sessions_table = f"{schema}.praison_sessions"
sql = f"CREATE SCHEMA IF NOT EXISTS {schema}"
print(f"[Postgres] VULN — schema injection:\n {sql}")
Output:
[SQLite] OK — rejected malicious table_prefix
[MySQL] VULN — generated SQL:
CREATE TABLE IF NOT EXISTS x'; DROP TABLE users; --sessions (session_id VARCHAR(255) PRIMARY KEY)
[Postgres] VULN — schema injection:
CREATE SCHEMA IF NOT EXISTS public; DROP SCHEMA data CASCADE; --
Vulnerable code (mysql.py, representative)
# mysql.py:65-67 — NO validation
self.table_prefix = table_prefix # ← raw input
self.sessions_table = f"{table_prefix}sessions" # ← into identifier
self.messages_table = f"{table_prefix}messages"
# mysql.py:105 — straight into DDL
cur.execute(f"""
CREATE TABLE IF NOT EXISTS {self.sessions_table} (
session_id VARCHAR(255) PRIMARY KEY, ...
)
""")
Compare with the patched sqlite.py:52:
# sqlite.py:52-53 — HAS validation
if not re.match(r'^[a-zA-Z0-9_]*$', table_prefix):
raise ValueError("table_prefix must contain only alphanumeric characters and underscores")
Impact
When table_prefix originates from untrusted input — multi-tenant tenant names, API request parameters, user-editable config — an attacker achieves arbitrary SQL execution against the backing database. The injected SQL runs in the context of DDL and DML operations (CREATE TABLE, INSERT, SELECT, DELETE), giving the attacker read/write/delete access to the entire database.
PostgreSQL's schema parameter adds a second injection vector in DDL (CREATE SCHEMA IF NOT EXISTS {schema}).
Severity
8.1 (High)
Impacted products
| Name | purl | praisonaiagents | pkg:pypi/praisonaiagents |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "praisonaiagents",
"purl": "pkg:pypi/praisonaiagents"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.6.8"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.0.1",
"0.0.10",
"0.0.100",
"0.0.101",
"0.0.102",
"0.0.103",
"0.0.104",
"0.0.105",
"0.0.106",
"0.0.107",
"0.0.108",
"0.0.109",
"0.0.11",
"0.0.110",
"0.0.111",
"0.0.112",
"0.0.113",
"0.0.114",
"0.0.115",
"0.0.116",
"0.0.117",
"0.0.118",
"0.0.119",
"0.0.12",
"0.0.120",
"0.0.121",
"0.0.122",
"0.0.123",
"0.0.124",
"0.0.125",
"0.0.126",
"0.0.127",
"0.0.128",
"0.0.129",
"0.0.13",
"0.0.130",
"0.0.131",
"0.0.132",
"0.0.133",
"0.0.134",
"0.0.135",
"0.0.136",
"0.0.137",
"0.0.138",
"0.0.139",
"0.0.14",
"0.0.140",
"0.0.141",
"0.0.142",
"0.0.143",
"0.0.144",
"0.0.145",
"0.0.146",
"0.0.147",
"0.0.148",
"0.0.149",
"0.0.15",
"0.0.150",
"0.0.151",
"0.0.152",
"0.0.153",
"0.0.154",
"0.0.155",
"0.0.156",
"0.0.157",
"0.0.158",
"0.0.159",
"0.0.16",
"0.0.160",
"0.0.161",
"0.0.162",
"0.0.163",
"0.0.164",
"0.0.165",
"0.0.166",
"0.0.167",
"0.0.168",
"0.0.169",
"0.0.17",
"0.0.170",
"0.0.171",
"0.0.172",
"0.0.173",
"0.0.174",
"0.0.175",
"0.0.176",
"0.0.177",
"0.0.178",
"0.0.179",
"0.0.18",
"0.0.180",
"0.0.181",
"0.0.182",
"0.0.183",
"0.0.184",
"0.0.185",
"0.0.187",
"0.0.188",
"0.0.189",
"0.0.19",
"0.0.190",
"0.0.191",
"0.0.192",
"0.0.193",
"0.0.194",
"0.0.195",
"0.0.196",
"0.0.197",
"0.0.198",
"0.0.199",
"0.0.2",
"0.0.20",
"0.0.21",
"0.0.22",
"0.0.23",
"0.0.24",
"0.0.25",
"0.0.26",
"0.0.27",
"0.0.28",
"0.0.29",
"0.0.3",
"0.0.30",
"0.0.31",
"0.0.32",
"0.0.33",
"0.0.34",
"0.0.35",
"0.0.36",
"0.0.37",
"0.0.38",
"0.0.39",
"0.0.4",
"0.0.40",
"0.0.41",
"0.0.42",
"0.0.43",
"0.0.44",
"0.0.45",
"0.0.46",
"0.0.47",
"0.0.48",
"0.0.49",
"0.0.5",
"0.0.50",
"0.0.51",
"0.0.52",
"0.0.53",
"0.0.54",
"0.0.56",
"0.0.57",
"0.0.58",
"0.0.59",
"0.0.6",
"0.0.60",
"0.0.61",
"0.0.62",
"0.0.63",
"0.0.64",
"0.0.65",
"0.0.66",
"0.0.67",
"0.0.68",
"0.0.69",
"0.0.7",
"0.0.70",
"0.0.71",
"0.0.72",
"0.0.73",
"0.0.74",
"0.0.75",
"0.0.76",
"0.0.77",
"0.0.78",
"0.0.79",
"0.0.8",
"0.0.80",
"0.0.81",
"0.0.82",
"0.0.83",
"0.0.84",
"0.0.85",
"0.0.86",
"0.0.87",
"0.0.88",
"0.0.89",
"0.0.9",
"0.0.90",
"0.0.91",
"0.0.92",
"0.0.93",
"0.0.94",
"0.0.95",
"0.0.96",
"0.0.97",
"0.0.98",
"0.0.99",
"0.1.0",
"0.1.1",
"0.1.10",
"0.1.11",
"0.1.12",
"0.1.13",
"0.1.14",
"0.1.15",
"0.1.16",
"0.1.17",
"0.1.18",
"0.1.19",
"0.1.2",
"0.1.20",
"0.1.21",
"0.1.22",
"0.1.23",
"0.1.24",
"0.1.25",
"0.1.26",
"0.1.27",
"0.1.3",
"0.1.4",
"0.1.5",
"0.1.6",
"0.1.7",
"0.1.8",
"0.1.9",
"0.10.0",
"0.10.1",
"0.10.10",
"0.10.2",
"0.10.3",
"0.10.4",
"0.10.5",
"0.10.6",
"0.10.7",
"0.10.8",
"0.10.9",
"0.11.0",
"0.11.1",
"0.11.10",
"0.11.11",
"0.11.12",
"0.11.13",
"0.11.14",
"0.11.15",
"0.11.16",
"0.11.17",
"0.11.18",
"0.11.19",
"0.11.2",
"0.11.20",
"0.11.21",
"0.11.22",
"0.11.23",
"0.11.24",
"0.11.25",
"0.11.27",
"0.11.28",
"0.11.29",
"0.11.3",
"0.11.30",
"0.11.31",
"0.11.4",
"0.11.5",
"0.11.6",
"0.11.7",
"0.11.8",
"0.11.9",
"0.12.0",
"0.12.1",
"0.12.10",
"0.12.11",
"0.12.12",
"0.12.13",
"0.12.14",
"0.12.15",
"0.12.16",
"0.12.17",
"0.12.18",
"0.12.19",
"0.12.2",
"0.12.20",
"0.12.21",
"0.12.3",
"0.12.4",
"0.12.5",
"0.12.6",
"0.12.7",
"0.12.8",
"0.12.9",
"0.13.0",
"0.13.1",
"0.13.10",
"0.13.11",
"0.13.12",
"0.13.13",
"0.13.14",
"0.13.15",
"0.13.16",
"0.13.17",
"0.13.18",
"0.13.19",
"0.13.2",
"0.13.20",
"0.13.21",
"0.13.22",
"0.13.23",
"0.13.3",
"0.13.4",
"0.13.5",
"0.13.6",
"0.13.7",
"0.13.8",
"0.13.9",
"0.14.0",
"0.14.1",
"0.14.10",
"0.14.11",
"0.14.12",
"0.14.14",
"0.14.15",
"0.14.16",
"0.14.2",
"0.14.3",
"0.14.4",
"0.14.5",
"0.14.6",
"0.14.7",
"0.14.8",
"0.14.9",
"0.15.0",
"0.15.1",
"0.15.2",
"0.15.3",
"0.2.0",
"0.2.1",
"0.2.2",
"0.3.0",
"0.3.1",
"0.3.2",
"0.3.3",
"0.3.4",
"0.4.0",
"0.4.1",
"0.5.0",
"0.5.1",
"0.5.2",
"0.5.3",
"0.6.0",
"0.6.1",
"0.6.2",
"0.6.3",
"0.6.4",
"0.6.5",
"0.6.6",
"0.6.7",
"0.6.8",
"0.7.0",
"0.7.1",
"0.8.0",
"0.8.1",
"0.9.0",
"0.9.1",
"1.0.0",
"1.1.0",
"1.2.0",
"1.2.1",
"1.2.2",
"1.2.3",
"1.2.4",
"1.3.0",
"1.3.1",
"1.4.0",
"1.4.1",
"1.4.2",
"1.4.3",
"1.4.4",
"1.4.5",
"1.4.6",
"1.4.7",
"1.4.8",
"1.5.0",
"1.5.1",
"1.5.10",
"1.5.100",
"1.5.101",
"1.5.102",
"1.5.103",
"1.5.104",
"1.5.105",
"1.5.106",
"1.5.107",
"1.5.108",
"1.5.109",
"1.5.11",
"1.5.110",
"1.5.111",
"1.5.112",
"1.5.113",
"1.5.114",
"1.5.115",
"1.5.116",
"1.5.117",
"1.5.118",
"1.5.119",
"1.5.12",
"1.5.120",
"1.5.121",
"1.5.122",
"1.5.123",
"1.5.124",
"1.5.125",
"1.5.126",
"1.5.127",
"1.5.128",
"1.5.129",
"1.5.13",
"1.5.130",
"1.5.131",
"1.5.132",
"1.5.133",
"1.5.134",
"1.5.135",
"1.5.136",
"1.5.137",
"1.5.138",
"1.5.139",
"1.5.14",
"1.5.140",
"1.5.141",
"1.5.142",
"1.5.143",
"1.5.144",
"1.5.145",
"1.5.146",
"1.5.147",
"1.5.148",
"1.5.149",
"1.5.15",
"1.5.16",
"1.5.17",
"1.5.18",
"1.5.19",
"1.5.2",
"1.5.20",
"1.5.21",
"1.5.22",
"1.5.23",
"1.5.24",
"1.5.25",
"1.5.26",
"1.5.27",
"1.5.28",
"1.5.29",
"1.5.3",
"1.5.30",
"1.5.31",
"1.5.32",
"1.5.33",
"1.5.34",
"1.5.35",
"1.5.36",
"1.5.37",
"1.5.38",
"1.5.39",
"1.5.40",
"1.5.41",
"1.5.42",
"1.5.43",
"1.5.44",
"1.5.45",
"1.5.46",
"1.5.47",
"1.5.48",
"1.5.49",
"1.5.5",
"1.5.50",
"1.5.51",
"1.5.52",
"1.5.53",
"1.5.54",
"1.5.55",
"1.5.56",
"1.5.57",
"1.5.58",
"1.5.59",
"1.5.6",
"1.5.60",
"1.5.61",
"1.5.62",
"1.5.63",
"1.5.64",
"1.5.65",
"1.5.66",
"1.5.67",
"1.5.68",
"1.5.69",
"1.5.7",
"1.5.70",
"1.5.71",
"1.5.72",
"1.5.73",
"1.5.74",
"1.5.75",
"1.5.76",
"1.5.77",
"1.5.78",
"1.5.79",
"1.5.8",
"1.5.80",
"1.5.81",
"1.5.82",
"1.5.83",
"1.5.84",
"1.5.85",
"1.5.86",
"1.5.87",
"1.5.88",
"1.5.89",
"1.5.9",
"1.5.90",
"1.5.91",
"1.5.92",
"1.5.93",
"1.5.94",
"1.5.95",
"1.5.96",
"1.5.97",
"1.5.98",
"1.5.99",
"1.6.1",
"1.6.2",
"1.6.3",
"1.6.4",
"1.6.5",
"1.6.6",
"1.6.7"
]
}
],
"aliases": [
"CVE-2026-41496",
"GHSA-rg3h-x3jw-7jm5"
],
"details": "The fix for [CVE-2026-40315](https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-x783-xp3g-mqhp) added input validation to `SQLiteConversationStore` only. Nine sibling backends \u2014 MySQL, PostgreSQL, async SQLite/MySQL/PostgreSQL, Turso, SingleStore, Supabase, SurrealDB \u2014 pass `table_prefix` straight into f-string SQL. Same root cause, same code pattern, same exploitation. 52 unvalidated injection points across the codebase.\n\n`postgres.py` additionally accepts an unvalidated `schema` parameter used directly in DDL.\n\n### Severity\n\n**High** \u2014 CWE-89 (SQL Injection)\n\nCVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N \u2014 **8.1**\n\nExploitable in any deployment where `table_prefix` is derived from external input (multi-tenant setups, API-driven configuration, user-modifiable config files). Default config (`\"praison_\"`) is not affected.\n\n### Details\n\nThe [CVE-2026-40315 fix](https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-x783-xp3g-mqhp) added this guard to `sqlite.py:52`:\n\n```python\n# sqlite.py \u2014 PATCHED\nimport re\nif not re.match(r\u0027^[a-zA-Z0-9_]*$\u0027, table_prefix):\n raise ValueError(\"table_prefix must contain only alphanumeric characters and underscores\")\n```\n\nThe following backends perform the identical `table_prefix \u2192 f-string SQL` pattern **without this guard**:\n\n| Backend | File | Line | Injection points |\n| ---------------- | -------------------------------------------- | --------------- | ----------------------- |\n| MySQL | `persistence/conversation/mysql.py` | 65 | 5 |\n| PostgreSQL | `persistence/conversation/postgres.py` | 89 (+schema:88) | 10 |\n| Async SQLite | `persistence/conversation/async_sqlite.py` | 43 | 13 |\n| Async MySQL | `persistence/conversation/async_mysql.py` | 65 | 13 |\n| Async PostgreSQL | `persistence/conversation/async_postgres.py` | 63 | 13 |\n| Turso/LibSQL | `persistence/conversation/turso.py` | 66 | 9 |\n| SingleStore | `persistence/conversation/singlestore.py` | 51 | 7 |\n| Supabase | `persistence/conversation/supabase.py` | 68 | 9 |\n| SurrealDB | `persistence/conversation/surrealdb.py` | 57 | 8 |\n| **Total** | **9 backends** | | **52 injection points** |\n\nAdditionally, `praisonai-agents/praisonaiagents/storage/backends.py:179` (`SQLiteBackend`) accepts `table_name` without validation.\n\n### PoC\n\n```python\n#!/usr/bin/env python3\n\"\"\"\nDemonstrates: sqlite.py rejects malicious table_prefix, mysql.py accepts it.\nRun: python3 poc.py (no dependencies required)\n\"\"\"\nimport re\n\npayload = \"x\u0027; DROP TABLE users; --\"\n\n# \u2500\u2500 SQLite (patched) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\ntry:\n if not re.match(r\u0027^[a-zA-Z0-9_]*$\u0027, payload):\n raise ValueError(\"blocked\")\n print(f\"[SQLite] FAIL \u2014 accepted: {payload}\")\nexcept ValueError:\n print(f\"[SQLite] OK \u2014 rejected malicious table_prefix\")\n\n# \u2500\u2500 MySQL (unpatched) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nsessions_table = f\"{payload}sessions\"\nsql = f\"CREATE TABLE IF NOT EXISTS {sessions_table} (session_id VARCHAR(255) PRIMARY KEY)\"\nprint(f\"[MySQL] VULN \u2014 generated SQL:\\n {sql}\")\n\n# \u2500\u2500 PostgreSQL (unpatched \u2014 both table_prefix AND schema) \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\nschema = \"public; DROP SCHEMA data CASCADE; --\"\nsessions_table = f\"{schema}.praison_sessions\"\nsql = f\"CREATE SCHEMA IF NOT EXISTS {schema}\"\nprint(f\"[Postgres] VULN \u2014 schema injection:\\n {sql}\")\n```\n\nOutput:\n\n```\n[SQLite] OK \u2014 rejected malicious table_prefix\n[MySQL] VULN \u2014 generated SQL:\n CREATE TABLE IF NOT EXISTS x\u0027; DROP TABLE users; --sessions (session_id VARCHAR(255) PRIMARY KEY)\n[Postgres] VULN \u2014 schema injection:\n CREATE SCHEMA IF NOT EXISTS public; DROP SCHEMA data CASCADE; --\n```\n\n### Vulnerable code (mysql.py, representative)\n\n```python\n# mysql.py:65-67 \u2014 NO validation\nself.table_prefix = table_prefix # \u2190 raw input\nself.sessions_table = f\"{table_prefix}sessions\" # \u2190 into identifier\nself.messages_table = f\"{table_prefix}messages\"\n\n# mysql.py:105 \u2014 straight into DDL\ncur.execute(f\"\"\"\n CREATE TABLE IF NOT EXISTS {self.sessions_table} (\n session_id VARCHAR(255) PRIMARY KEY, ...\n )\n\"\"\")\n```\n\nCompare with the patched `sqlite.py:52`:\n\n```python\n# sqlite.py:52-53 \u2014 HAS validation\nif not re.match(r\u0027^[a-zA-Z0-9_]*$\u0027, table_prefix):\n raise ValueError(\"table_prefix must contain only alphanumeric characters and underscores\")\n```\n\n### Impact\n\nWhen `table_prefix` originates from untrusted input \u2014 multi-tenant tenant names, API request parameters, user-editable config \u2014 an attacker achieves **arbitrary SQL execution** against the backing database. The injected SQL runs in the context of DDL and DML operations (CREATE TABLE, INSERT, SELECT, DELETE), giving the attacker read/write/delete access to the entire database.\n\nPostgreSQL\u0027s `schema` parameter adds a second injection vector in DDL (`CREATE SCHEMA IF NOT EXISTS {schema}`).",
"id": "PYSEC-2026-2952",
"modified": "2026-07-13T16:05:45.388413Z",
"published": "2026-07-13T15:02:49.618282Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/MervinPraison/PraisonAI/security/advisories/GHSA-rg3h-x3jw-7jm5"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-41496"
},
{
"type": "PACKAGE",
"url": "https://github.com/MervinPraison/PraisonAI"
},
{
"type": "PACKAGE",
"url": "https://pypi.org/project/praisonaiagents"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-rg3h-x3jw-7jm5"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N",
"type": "CVSS_V3"
}
],
"summary": "PraisonAI: SQL Injection via unvalidated `table_prefix` in 9 conversation store backends (incomplete fix for CVE-2026-40315)"
}
Loading…
Loading…
Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
Loading…
Loading…