SQL Injection in Multi-Agent Database Backends

PraisonAI is a multi-agent teams system. Prior to praisonai version 4.6.9 and praisonaiagents version 1.6.9, 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. This issue has been patched in praisonai version 4.6.9 and praisonaiagents version 1.6.9.

Casky was already ahead

This CVE exploits attack patterns that Casky's 301matched skills already investigate — long before this vulnerability was disclosed. Claude's reasoning model maps these techniques to MITRE ATT&CK, so practitioners who ran these skills have already seen the threat behaviour in their findings.

Analysis

CVE-2026-41496 exposes a critical SQL injection vulnerability across nine database backends in PraisonAI's multi-agent system. While a partial fix addressed SQLiteConversationStore in CVE-2026-40315, the same vulnerable code pattern—passing unsanitized table_prefix parameters directly into f-string SQL queries—persists across MySQL, PostgreSQL, async variants, Turso, SingleStore, Supabase, and SurrealDB implementations. With 52 injection points and a CVSS score of 8.1, this affects any organization deploying PraisonAI versions before 4.6.9 (praisonai) and 1.6.9 (praisonaiagents). Attackers can manipulate table_prefix inputs to inject arbitrary SQL, potentially leading to unauthorized data access, modification, or exfiltration from backend databases.

Casky's 301 matching skills leverage Claude's extended reasoning to correlate SQL injection patterns (CWE-89) with MITRE ATT&CK techniques TA0001 (Reconnaissance) and TA0043 (Resource Development). Practitioners using Casky would detect this threat through skills mapping input validation failures, dynamic SQL construction without parameterized queries, and inconsistent patch application across sibling database modules. The platform's multi-skill analysis would flag suspicious table_prefix parameters in logs, identify when f-string interpolation is used in database operations, and surface the architectural inconsistency where only one of nine backends received the security patch—exposing the systemic nature of this injection vulnerability before exploitation occurs.

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Casky Risk Score

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EPSS Probability

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Shodan Exposure

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GreyNoise Signal

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Composite risk scoring from EPSS, CISA KEV, Shodan, and GreyNoise — 21 security APIs correlated into a single Casky Risk Score. Coming in Casky Pro. Join early access →

301 Casky skills cover this attack pattern

These skills use Claude AI's reasoning model to surface findings in the same attack categories as CVE-2026-41496.

analyzing-apt-group-with-mitre-navigator

threat intelligence · low

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analyzing-campaign-attribution-evidence

threat intelligence · low

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analyzing-certificate-transparency-for-phishing

threat intelligence · low

Run

MITRE ATT&CK Techniques

TA0001 TA0043

Investigate the attack patterns behind CVE-2026-41496

Casky has 301 skills that investigate the attack patterns behind CVE-2026-41496. Run one and get CVSS-scored findings in 3 minutes.

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