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Langroid: SQLChatAgent dangerous-function blocklist can be bypassed with quoted or schema-qualified pg_read_file calls

Critical severity GitHub Reviewed Published Jun 9, 2026 in langroid/langroid • Updated Jul 6, 2026

Package

pip langroid (pip)

Affected versions

<= 0.65.0

Patched versions

0.65.1

Description

SQLChatAgent _validate_query dangerous-pattern regex is bypassable via quoted/commented/qualified function names

Summary

The SQLChatAgent SQL-injection mitigation, with default allow_dangerous_operations=False, combines a raw-text regex blocklist (_DANGEROUS_SQL_PATTERNS) with a sqlglot SELECT-only statement allowlist. The blocklist entries that target callable functions require the function name to be immediately followed by \s*\(.

PostgreSQL accepts the same call with the name separated from ( by a quoted identifier, an inline comment, or schema qualification. These forms evade the regex, still parse as SELECT, and execute the same PostgreSQL function. This restores the pg_read_file server-side file-read primitive that the prior CVE-2026-25879 / GHSA-pmch-g965-grmr fix was meant to block: the parent advisory fixed a missing pg_read_file blocklist entry, while this report shows that the added regex is bypassable.

Affected Code

Tested against current main commit:

6e8e7b2bb23ec04c1c25be479f16b8cc9a4f8796

The current source still contains:

re.compile(r"\bpg_(read|stat|ls|current_logfile)[A-Za-z0-9_]*\s*\(", re.IGNORECASE)

_validate_query checks the raw query against _DANGEROUS_SQL_PATTERNS, then parses with sqlglot and allows SELECT statements. The dangerous-call check is raw text, not normalized AST function-name matching.

Root Cause

The current mitigation treats dangerous PostgreSQL function calls as a raw-text regex problem. The regex requires the pg_... function token to be followed directly by optional whitespace and (, but PostgreSQL accepts equivalent calls through quoted identifiers, comments, and schema-qualified names. Because _validate_query only uses sqlglot to enforce the top-level statement type, those normalized function names are never checked after parsing.

Auth Boundary

The boundary is the default SQLChatAgent safety policy between attacker-influenced SQL generation and database operations that can read server-side files. With allow_dangerous_operations=False, a user or prompt that influences generated SQL should not be able to bypass the guard and execute PostgreSQL file-read functions such as pg_read_file.

This is not a new unauthenticated endpoint or product-wide SQL injection; it applies when untrusted user content can influence SQLChatAgent's generated SQL.

Reproduction

The local harness uses the current sql_chat_agent.py, extracts the real shipped dangerous regex list, validates the queries with real sqlglot==30.8.0, then executes the accepted bypasses against a local throwaway PostgreSQL 16 container.

Transcript excerpt:

CONTROL   "SELECT pg_read_file('/etc/passwd')" -> REJECTED: matches '\\bpg_(read|stat|ls|current_logfile)[A-Za-z0-9_]*\\s*\\('
BYPASS    'SELECT "pg_read_file"(\'/etc/passwd\')' -> ALLOWED (validator returned None -> would execute)
BYPASS    "SELECT pg_read_file/**/('/etc/passwd')" -> ALLOWED (validator returned None -> would execute)
BYPASS    'SELECT pg_catalog."pg_read_file"(\'/etc/passwd\')' -> ALLOWED (validator returned None -> would execute)

=== Part B: real PostgreSQL execution of the bypass ===
connected; is_superuser=t
  executed bypass 'SELECT "pg_read_file"(\'<file>\')' -> file contents returned: 'LANGROID_SAFE_MARKER_...'
  executed bypass "SELECT pg_read_file/**/('<file>')" -> file contents returned: 'LANGROID_SAFE_MARKER_...'
  executed bypass 'SELECT pg_catalog."pg_read_file"(\'<file>\')' -> file contents returned: 'LANGROID_SAFE_MARKER_...'

RESULT: VULNERABLE

The control query is blocked by the current regex, while all three equivalent PostgreSQL forms are allowed by the validator and return the mounted proof file contents from a real PostgreSQL server. The LANGROID_SAFE_MARKER_... value is a harmless marker generated inside the throwaway local container for this proof.

Impact

On a deployment using SQLChatAgent against PostgreSQL with a role able to call pg_read_file (superuser, or a role granted pg_read_server_files), an attacker who can influence LLM-generated SQL can coerce the agent into emitting one of the obfuscated queries and read files accessible to the PostgreSQL server process through pg_read_file.

This is the same impact and precondition shape as the published pg_read_file advisory, but it targets the bypassability of the current regex-based fix rather than the pre-fix absence of a pg_read_file block.

Severity: High by parity with the published parent advisory; not Critical. CWE-184 leading to server-side file read.

Suggested Fix

Do not rely on raw-text regex matching for dangerous-call detection. After the existing sqlglot parse, walk the AST and reject any function invocation whose normalized, unquoted, schema-stripped, case-folded name is in a dangerous set such as pg_read_file, pg_read_binary_file, pg_ls_dir, pg_stat_file, lo_import, lo_export, load_file, or load_extension.

Also recommend running SQLChatAgent with a least-privilege database role that lacks pg_read_server_files.

References

@pchalasani pchalasani published to langroid/langroid Jun 9, 2026
Published to the GitHub Advisory Database Jul 6, 2026
Reviewed Jul 6, 2026
Last updated Jul 6, 2026

Severity

Critical

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 High
Integrity High
Availability High
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:H/VI:H/VA:H/SC:N/SI:N/SA:N

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(47th percentile)

Weaknesses

Improper Limitation of a Pathname to a Restricted Directory ('Path Traversal')

The product uses external input to construct a pathname that is intended to identify a file or directory that is located underneath a restricted parent directory, but the product does not properly neutralize special elements within the pathname that can cause the pathname to resolve to a location that is outside of the restricted directory. Learn more on MITRE.

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

CVE-2026-54760

GHSA ID

GHSA-6xc5-4r68-67fc

Source code

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