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    <link>https://db.gcve.eu</link>
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    <lastBuildDate>Mon, 19 Oct 2026 21:08:29 +0000</lastBuildDate>
    <item>
      <title>CVE-2026-7700 — langflow-ai langflow LambdaFilterComponent lambda_filter.py eval code injection</title>
      <link>https://db.gcve.eu/vuln/cve-2026-7700</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; langflow-ai langflow&lt;/p&gt;
&lt;p&gt;A weakness has been identified in langflow-ai langflow up to 1.10.2. This affects the function eval of the file src/lfx/src/lfx/components/llm_operations/lambda_filter.py of the component LambdaFilterComponent. Executing a manipulation can lead to code injection. The attack may be performed from remote. The exploit has been made available to the public and could be used for attacks. The vendor was contacted early about this disclosure but did not respond in any way.&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; langflow-ai langflow&lt;/p&gt;
&lt;p&gt;A weakness has been identified in langflow-ai langflow up to 1.10.2. This affects the function eval of the file src/lfx/src/lfx/components/llm_operations/lambda_filter.py of the component LambdaFilterComponent. Executing a manipulation can lead to code injection. The attack may be performed from remote. The exploit has been made available to the public and could be used for attacks. The vendor was contacted early about this disclosure but did not respond in any way.&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://db.gcve.eu/vuln/cve-2026-7700</guid>
    </item>
    <item>
      <title>PYSEC-2026-4232 — Langflow: Prompt injection in Langflow Smart Transform can lead to code execution</title>
      <link>https://db.gcve.eu/vuln/pysec-2026-4232</link>
      <description>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: langflow&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;Langflow versions 1.3.0 through 1.10.2 contain a code-injection vulnerability in the Smart Transform (`LambdaFilterComponent`) component.&lt;/p&gt;
&lt;p&gt;Smart Transform places flow-author instructions and a preview of its input data into a prompt asking an LLM to generate a Python lambda. It then extracts a one-line lambda from the model response, applies only syntactic format checks, evaluates it with Python&amp;#39;s full builtins, and invokes the resulting function inside the Langflow process.&lt;/p&gt;
&lt;p&gt;A malicious flow author can exploit this directly through the Instructions field. In deployments where an exposed flow passes attacker-controlled content into Smart Transform, an attacker may also exploit it indirectly through prompt injection, subject to the configured model following the injected instruction.&lt;/p&gt;
&lt;p&gt;## Vulnerability details&lt;/p&gt;
&lt;p&gt;**Vulnerable Code Location**: `src/lfx/src/lfx/components/llm_operations/lambda_filter.py` (line 242 in v1.10.2)&lt;/p&gt;
&lt;p&gt;```python
def _validate_lambda(self, lambda_text: str) -&amp;gt; bool:
    &amp;#34;&amp;#34;&amp;#34;Validate the provided lambda function text.&amp;#34;&amp;#34;&amp;#34;
    return lambda_text.strip().startswith(&amp;#34;lambda&amp;#34;) and &amp;#34;:&amp;#34; in lambda_text&lt;/p&gt;
&lt;p&gt;# ...
return eval(lambda_text)  # noqa: S307
```&lt;/p&gt;
&lt;p&gt;For example, an attacker can attempt to make the model return:&lt;/p&gt;
&lt;p&gt;`lambda x: __import__(&amp;#34;os&amp;#34;).system(&amp;#34;id&amp;#34;)`&lt;/p&gt;
&lt;p&gt;This expression satisfies the vulnerable format checks. `eval()` creates the lambda with access to Python&amp;#39;s default builtins, and the subsequent `fn(data)` invocation (in `_execute_lambda`) executes th…&lt;/p&gt;</description>
      <content:encoded>&lt;p&gt;&lt;strong&gt;Affected:&lt;/strong&gt; PyPI: langflow&lt;/p&gt;
&lt;p&gt;## Summary&lt;/p&gt;
&lt;p&gt;Langflow versions 1.3.0 through 1.10.2 contain a code-injection vulnerability in the Smart Transform (`LambdaFilterComponent`) component.&lt;/p&gt;
&lt;p&gt;Smart Transform places flow-author instructions and a preview of its input data into a prompt asking an LLM to generate a Python lambda. It then extracts a one-line lambda from the model response, applies only syntactic format checks, evaluates it with Python&amp;#39;s full builtins, and invokes the resulting function inside the Langflow process.&lt;/p&gt;
&lt;p&gt;A malicious flow author can exploit this directly through the Instructions field. In deployments where an exposed flow passes attacker-controlled content into Smart Transform, an attacker may also exploit it indirectly through prompt injection, subject to the configured model following the injected instruction.&lt;/p&gt;
&lt;p&gt;## Vulnerability details&lt;/p&gt;
&lt;p&gt;**Vulnerable Code Location**: `src/lfx/src/lfx/components/llm_operations/lambda_filter.py` (line 242 in v1.10.2)&lt;/p&gt;
&lt;p&gt;```python
def _validate_lambda(self, lambda_text: str) -&amp;gt; bool:
    &amp;#34;&amp;#34;&amp;#34;Validate the provided lambda function text.&amp;#34;&amp;#34;&amp;#34;
    return lambda_text.strip().startswith(&amp;#34;lambda&amp;#34;) and &amp;#34;:&amp;#34; in lambda_text&lt;/p&gt;
&lt;p&gt;# ...
return eval(lambda_text)  # noqa: S307
```&lt;/p&gt;
&lt;p&gt;For example, an attacker can attempt to make the model return:&lt;/p&gt;
&lt;p&gt;`lambda x: __import__(&amp;#34;os&amp;#34;).system(&amp;#34;id&amp;#34;)`&lt;/p&gt;
&lt;p&gt;This expression satisfies the vulnerable format checks. `eval()` creates the lambda with access to Python&amp;#39;s default builtins, and the subsequent `fn(data)` invocation (in `_execute_lambda`) executes th…&lt;/p&gt;</content:encoded>
      <guid isPermaLink="false">https://db.gcve.eu/vuln/pysec-2026-4232</guid>
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