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  <updated>2026-10-03T09:32:10.425746+00:00</updated>
  <author>
    <name>Vulnerability-Lookup</name>
    <email>csirt@opendfir.org</email>
  </author>
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  <entry>
    <id>https://cve.radiocsirt.org/vuln/cve-2026-55615</id>
    <title>CVE-2026-55615 — Langroid: Neo4jChatAgent executes LLM-generated Cypher without validation (prompt-to-Cypher injection; config-condition…</title>
    <updated>2026-10-03T09:32:10.457680+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml"><p><strong>Affected:</strong> langroid</p>
<p>Langroid is a framework for building large-language-model-powered applications. Prior to version 0.65.5, Neo4jChatAgent passes LLM-generated Cypher queries straight to the Neo4j driver with no validation, no statement-type allowlist, and no opt-out gate. The query text is influenceable by prompt injection (direct user input or indirect content the agent reads back via RAG), so an attacker who can influence the prompt can read or destroy all graph data and, when APOC or dbms.security procedures are enabled on the server, achieve OS-command and filesystem access. This is the same defect class and threat model as the SQLChatAgent prompt-to-SQL-to-RCE issue fixed in version 0.63.0 (CVE-2026-25879); that fix did not extend to the neo4j module. Version 0.65.5 contains a fix for the neo4j module.</p></div>
    </content>
    <link href="https://cve.radiocsirt.org/vuln/cve-2026-55615"/>
  </entry>
  <entry>
    <id>https://cve.radiocsirt.org/vuln/ghsa-2pq5-3q89-j7cc</id>
    <title>GHSA-2pq5-3q89-j7cc — Langroid: Neo4jChatAgent executes LLM-generated Cypher without validation (prompt-to-Cypher injection; config-condition…</title>
    <updated>2026-10-03T09:32:10.457745+00:00</updated>
    <content type="xhtml">
      <div xmlns="http://www.w3.org/1999/xhtml"><p><strong>Affected:</strong> PyPI: langroid</p>
<p>Neo4jChatAgent passes LLM-generated Cypher queries straight to the Neo4j driver with no validation, no statement-type allowlist, and no opt-out gate. The query text is influenceable by prompt injection (direct user input or indirect content the agent reads back via RAG), so an attacker who can influence the prompt can read or destroy all graph data and, when APOC or dbms.security procedures are enabled on the server, achieve OS-command and filesystem access. This is the same defect class and threat model as the SQLChatAgent prompt-to-SQL-to-RCE issue fixed in version 0.63.0 (CVE-2026-25879); that fix did not extend to the neo4j module.</p>
<p>## Technical detail
Untrusted-input to sink trace (reviewed on langroid HEAD b9df06f, v0.65.3):</p>
<p>1. Tool schemas accept raw query text from the LLM. `langroid/agent/special/neo4j/tools.py:4-9` (CypherRetrievalTool.cypher_query: str) and `:15-21` (CypherCreationTool.cypher_query: str). These tools are enabled unconditionally in `neo4j_chat_agent.py:412-419` (enable_message([GraphSchemaTool, CypherRetrievalTool, CypherCreationTool, DoneTool])).</p>
<p>2. Read path. `neo4j_chat_agent.py:300` cypher_retrieval_tool(msg) -&gt; `:325` query = msg.cypher_query -&gt; `:328` self.read_query(query) -&gt; `:223` session.run(query, parameters). The LLM-controlled string is the first positional argument to session.run; parameters is None. No validation occurs between :325 and :223.</p>
<p>3. Write path. `neo4j_chat_agent.py:338` cypher_creation_tool(msg) -&gt; `:348` query = msg.…</p></div>
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    <link href="https://cve.radiocsirt.org/vuln/ghsa-2pq5-3q89-j7cc"/>
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