GHSA-rg3h-x3jw-7jm5 CVE-2026-41496 PYSEC-2026-2923 PYSEC-2026-2952
PraisonAI: SQL Injection via unvalidated `table_prefix` in 9 conversation store backends (incomplete fix for CVE-2026-40315)
- Severity
- 8.1 / 10
- High · CVSS 3.1
- Exploit chance (EPSS)
- 0.35%
- Chance of exploitation in the next 30 days. Higher than 28.32% of scored vulnerabilities.
- Known exploited
- No
- Not in CISA's catalog
- Weakness
- CWE-89 Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
- Fix
- Available
- 2 packages have a fixed version
Description
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}).
Affected versions
PyPI
| Package | Vulnerable | Fixed in |
|---|---|---|
| praisonai | < 4.5.149 |
4.5.149 |
| praisonaiagents | < 1.6.8 |
1.6.8 |
How to fix
Move to the fixed version or later with your package manager, update the lockfile, and run your tests. If the package only arrives through another dependency, upgrade that dependency first. If none of its releases pulls a fixed version yet, force the version with your package manager's override.
praisonai PyPI → 4.5.149
- pip
pip install --upgrade "praisonai>=4.5.149"- poetry
poetry add "praisonai>=4.5.149"- uv
uv add "praisonai>=4.5.149"
praisonaiagents PyPI → 1.6.8
- pip
pip install --upgrade "praisonaiagents>=1.6.8"- poetry
poetry add "praisonaiagents>=1.6.8"- uv
uv add "praisonaiagents>=1.6.8"
Severity breakdown
CVSS 3.1 8.1
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N
- Attack vector
- Network
- Attack complexity
- Low
- Privileges required
- Low
- User interaction
- None
- Scope
- Unchanged
- Confidentiality
- High
- Integrity
- High
- Availability
- None
Weakness
CWE-89: Improper Neutralization of Special Elements used in an SQL Command ('SQL Injection')
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.
How to prevent it
- Architecture and Design: 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.
- Architecture and Design: 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]
- Architecture and Design, Operation: 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.
- Architecture and Design: For any security checks that are performed on the client side, ensure that these checks are duplicated on the server side, in order to avoid CWE-602. Attackers can bypass the client-side checks by modifying values after the checks have been performed, or by changing the client to remove the client-side checks entirely. Then, these modified values would be submitted to the server.
- Implementation: 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.
- Implementation: 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 data
- Architecture and Design: 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.
- Implementation: Ensure that error messages only contain minimal details that are useful to the intended audience and no one else. The messages need to strike the balance between being too cryptic (which can confuse users) or being too detailed (which may reveal more than intended). The messages should not reveal the methods that were used to determine the error. Attackers can use detailed information to refine or optimize their original attack, thereby increasing their chances of success. If errors must be captured in some detail, record them in log messages, but consider what could occur if the log messages can be viewed by attackers. Highly sensitive information such as passwords should never be saved to log files. Avoid inconsistent messaging that might accidentally tip off an attacker about internal state, such as whether a user account exists or not. In the context of SQL Injection, error messages revealing the structure of a SQL query can help attackers tailor successful attack strings.
- Operation: 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.
- Operation, Implementation: 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.
References
Advisories
Packages
Other links
Timeline
- Published
- Last updated by the source
- Latest EPSS score
Related advisories
| Advisory | Severity | Why it's related |
|---|---|---|
| GHSA-26mh-57q7-jfvr PraisonAI vulnerable to Remote Code Execution via Broken AST Sandbox | High | Also affects praisonai |
| GHSA-6g6r-q6gw-w8fg PraisonAI has a Browser Server WebSocket origin validation bypass via unanchored regex (patch bypass of CVE-2026-40289 / GHSA-8x8f-54wf-vv92) | Critical | Also affects praisonai |
| GHSA-pvph-5j39-v8qc PraisonAI: Origin-validation bypass (startswith prefix match) enables unauthenticated cross-site request forgery against the PraisonAI MCP HTTP server | High | Also affects praisonai |
| GHSA-gfq8-hmph-9gjv PraisonAI: Authentication fail-open in Recipe server allows unauthenticated access when API key or JWT auth is configured without a secret | High | Also affects praisonai |
| GHSA-2jgc-f764-c5r2 PraisonAI: [Auth Bypass] PraisonAI async Jobs API (`/api/v1/runs`) has no authentication — unauthenticated job execution, result theft, cancel and delete | High | Also affects praisonai |
| GHSA-gxmw-5f7x-6g22 praisonaiagents vulnerable to arbitrary file write via unsanitized `user_id` in `FileMemory.__init__()` — path traversal to any writable location | High | Also affects praisonaiagents |
| GHSA-pvxx-r596-f5qj PraisonAI: `--api-key` flag on `praisonai serve` is not properly enforced | High | Also affects praisonai |
| GHSA-hmfx-4v44-9qw9 PraisonAI vulnerable to Server-Side Request Forgery via DNS rebinding bypass in webhook_url validation | Medium | Also affects praisonai |
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