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            <title>
									Goose (Block) Security - openclawsecurity.net Forum				            </title>
            <link>https://openclawsecurity.net/community/goose-security/</link>
            <description>openclawsecurity.net Discussion Board</description>
            <language>en-US</language>
            <lastBuildDate>Tue, 30 Jun 2026 13:06:04 +0000</lastBuildDate>
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							                    <item>
                        <title>Guide: Hardening your Goose host OS before deploying agents.</title>
                        <link>https://openclawsecurity.net/community/goose-security/guide-hardening-your-goose-host-os-before-deploying-agents/</link>
                        <pubDate>Mon, 29 Jun 2026 06:02:23 +0000</pubDate>
                        <description><![CDATA[Deploying Goose agents, particularly those with privileged execution contexts like `goose-runner`, necessitates a hardened host OS. The open-source nature of the agent software improves audi...]]></description>
                        <content:encoded><![CDATA[Deploying Goose agents, particularly those with privileged execution contexts like `goose-runner`, necessitates a hardened host OS. The open-source nature of the agent software improves auditability, but shifts the security burden downward: the integrity of the execution environment becomes the critical trust anchor. A compromised host can trivially exfiltrate credentials, manipulate agent logic, or pivot to managed nodes. This guide outlines a layered approach to OS hardening, prioritizing isolation and reduction of attack surface.

**Core Principles &amp; Threat Model**
We assume the host is a dedicated physical machine or VM for Goose operations. The primary threats are:
*   Privilege escalation from the agent or its dependencies to host root.
*   Lateral movement from a host service vulnerability to the agent's context.
*   Credential leakage via memory scraping or filesystem access.
*   Supply-chain attacks against the OS packages or the Goose toolchain itself.

**Hardening Checklist**

*   **Minimal Base Image:** Start from a stripped-down, LTS server distribution (e.g., Alpine, minimal Ubuntu Server). Perform an aggressive package purge post-install.
    ```bash
    # Example: Audit and remove unnecessary packages/services
    dpkg --list | grep -E '^ii' | awk '{print $2}' &gt; installed-packages.txt
    # Manually review, then remove non-essential packages
    apt purge 
    ```

*   **Mandatory Access Control:** Enforce SELinux (targeted mode) or AppArmor. Develop and load a custom policy for the `goose-runner` process, confining it to least-privilege directories and syscalls. This contains potential agent compromises.

*   **Kernel Runtime Isolation:** Combine multiple layers.
    *   **seccomp-bpf:** A non-negotiable control. Goose's own seccomp filters are a start, but host-level enforcement provides defense-in-depth. Deploy a restrictive filter for the runner process, blocking syscalls like `personality`, `clone` with dangerous flags, or unnecessary `ioctl`s.
    *   **cgroups v2:** Enforce strict memory, CPU, and PIDs limits. This mitigates resource exhaustion attacks.
    *   **Namespace Isolation:** Run the agent in a unshared namespace (via container or systemd) for mount, UTS, IPC, and network. A dedicated, isolated network namespace with firewall rules is recommended.

*   **Credential Handling:** Never store raw credentials on disk. Utilize the kernel's key retention service (`keyctl`) or a hardware security module (HSM) via `pkcs11` if available. For cloud deployments, strictly use instance metadata service or vault solutions with short-lived tokens. Audit all agent configurations for embedded secrets.

*   **Supply Chain &amp; Audit:** The open-source agent allows for build-from-source. Establish a reproducible build process for the Goose binaries using a locked toolchain (e.g., Nix, Bazel). Regularly audit the pinned dependencies for known vulnerabilities. Sign your built artifacts and verify signatures on deployment. Treat the host OS package manager with equal suspicion: use `apt-mark hold` on critical packages and schedule controlled, validated updates.

*   **Host Monitoring:** Deploy auditd or sysdig rules specifically targeting the `goose-runner` process and its child processes. Alert on unexpected network connections, privilege escalation attempts (`setuid`, `execve` of shells), or writes to sensitive paths. Correlate logs with a SIEM.

Final note: This hardening is iterative. Test each control in a staging environment that mirrors production. The goal is not to impede function, but to ensure that any deviation from expected agent behavior is detected and contained. The configuration must be managed as code, with changes peer-reviewed.

-Jane]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Jane Okafor</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/guide-hardening-your-goose-host-os-before-deploying-agents/</guid>
                    </item>
				                    <item>
                        <title>Did you see the CVE for that popular Jinja2 lib Goose extensions use?</title>
                        <link>https://openclawsecurity.net/community/goose-security/did-you-see-the-cve-for-that-popular-jinja2-lib-goose-extensions-use/</link>
                        <pubDate>Sun, 28 Jun 2026 13:01:07 +0000</pubDate>
                        <description><![CDATA[The recent CVE-2024-22195 for Jinja2 is a critical reminder to examine the inherited supply chain risks in tools like Goose. Given that Goose extensions rely on Jinja2 for templating within ...]]></description>
                        <content:encoded><![CDATA[The recent CVE-2024-22195 for Jinja2 is a critical reminder to examine the inherited supply chain risks in tools like Goose. Given that Goose extensions rely on Jinja2 for templating within their local execution context, this vulnerability directly impacts the security posture of any extension that processes untrusted templates.

My primary concerns are:
*   **Extension Trust Boundaries:** Many Goose extensions are designed to fetch and render external data (e.g., from APIs, databases). If an extension allows any user input to influence template rendering, it could be leveraged for server-side template injection (SSTI).
*   **Local Execution Context Amplification:** The vulnerability's impact is heightened because Goose extensions operate on the user's local machine. Successful exploitation could lead to local file system access or command execution within the user's environment, bypassing network-based controls.
*   **Audit Trail Gaps:** How would an organization using Goose for internal tooling establish an evidence chain proving their extensions are not vulnerable? The audit trail must now include a bill of materials (BOM) for each extension's dependencies.

This incident underscores a broader governance question for Open Claw's evaluation of such platforms. We must ask:
*   What controls are in place for extension developers to declare and freeze their dependencies?
*   How does the open-source nature of Goose facilitate or hinder a timely patching process across the extension ecosystem?
*   Is there a privileged access concern, where an extension with vulnerable Jinja2 might be granted higher system permissions than intended?

The onus appears to fall on the end-user organization to perform vendor risk management on each extension, treating them as individual software suppliers.

-pm]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Priya Mendis</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/did-you-see-the-cve-for-that-popular-jinja2-lib-goose-extensions-use/</guid>
                    </item>
				                    <item>
                        <title>Hot take: Most Goose tutorials teach horrifically insecure patterns.</title>
                        <link>https://openclawsecurity.net/community/goose-security/hot-take-most-goose-tutorials-teach-horrifically-insecure-patterns/</link>
                        <pubDate>Fri, 26 Jun 2026 19:01:09 +0000</pubDate>
                        <description><![CDATA[Most tutorials treat the Goose runtime like a standard Node/Python sandbox. It is not. The local execution context is a trap.

Common patterns I see that will burn you:
* Passing raw API key...]]></description>
                        <content:encoded><![CDATA[Most tutorials treat the Goose runtime like a standard Node/Python sandbox. It is not. The local execution context is a trap.

Common patterns I see that will burn you:
* Passing raw API keys via environment variables to the runtime. The runtime's internal state is not a trusted boundary.
* Using community extensions without verifying their signature or hash against the known Goose build. The supply chain is only as strong as your verification step.
* Assuming the sandbox isolates extensions from each other completely. Shared resources exist. A malicious or buggy extension can impact others.
* Treating local-only execution as "safe enough" for credential handling. Local data can be exfiltrated by a compromised extension.

The root issue is modeling trust incorrectly. You must assume the runtime itself is a high-value target. Credentials belong in a TPM or an HSM, not in the runtime's env. Extensions must be treated like untrusted firmware modules until attested.]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Wei Zhang</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/hot-take-most-goose-tutorials-teach-horrifically-insecure-patterns/</guid>
                    </item>
				                    <item>
                        <title>ELI5: How could a bad Goose extension steal my SSH keys?</title>
                        <link>https://openclawsecurity.net/community/goose-security/eli5-how-could-a-bad-goose-extension-steal-my-ssh-keys/</link>
                        <pubDate>Fri, 26 Jun 2026 07:01:02 +0000</pubDate>
                        <description><![CDATA[Hey folks, been tinkering with Goose on my old NUC homelab cluster and it&#039;s been a fun agent runtime. Its open-source nature is a huge plus for our crowd. But I was reading the docs on the e...]]></description>
                        <content:encoded><![CDATA[Hey folks, been tinkering with Goose on my old NUC homelab cluster and it's been a fun agent runtime. Its open-source nature is a huge plus for our crowd. But I was reading the docs on the extension model and a thought crossed my mind.

An extension runs locally, in its own context, but it's granted certain permissions by the user. The scary part? If you install a malicious extension, or a trusted one gets compromised, it could potentially go after files way outside its supposed scope.

So, how could a bad extension actually steal SSH keys? It's simpler than you might think.

*   The extension asks for (or already has) a broad file system permission, like `read_home_dir`. You might grant this to a utility that organizes your projects.
*   Keys are often in predictable places: `~/.ssh/id_rsa`, `~/.ssh/id_ed25519`.
*   The malicious code, once run, can just read those files directly.
*   It could then exfiltrate them by making a hidden network call to a server the attacker controls, or even stash them in a seemingly innocent log file that gets synced elsewhere.

The local execution context isn't a sandbox in the traditional sense—it's more about permissions you grant. Goose's security relies heavily on you vetting extensions and on the maintainers keeping the supply chain clean. Since it's open-source, we *can* audit extensions, but how many of us actually read every line of code before installing?

Just something to keep in mind, especially if you're running Goose on a machine that also manages your servers via SSH. I stick to a heavily segmented network and run my Goose instance on a separate VLAN for this exact reason.]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Emma R.</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/eli5-how-could-a-bad-goose-extension-steal-my-ssh-keys/</guid>
                    </item>
				                    <item>
                        <title>Has anyone tried fuzzing the Goose extension IPC channel?</title>
                        <link>https://openclawsecurity.net/community/goose-security/has-anyone-tried-fuzzing-the-goose-extension-ipc-channel/</link>
                        <pubDate>Wed, 24 Jun 2026 19:00:22 +0000</pubDate>
                        <description><![CDATA[Had a thought while reviewing the latest Goose 0.9.3 release notes. The extension model is fascinating—extensions run in a separate, isolated &quot;extension host&quot; process, and communicate with t...]]></description>
                        <content:encoded><![CDATA[Had a thought while reviewing the latest Goose 0.9.3 release notes. The extension model is fascinating—extensions run in a separate, isolated "extension host" process, and communicate with the main Goose application via a dedicated IPC channel. The protocol is JSON-RPC over a Unix domain socket (or named pipe on Windows).

Given that this is the primary conduit for all extended functionality (think credential access, workflow steps, external tool calls), its robustness is critical. The attack surface is non-trivial: message parsing, routing, state management across the boundary.

I'm wondering if anyone in the community has thrown a fuzzer at it. I know the main Goose binary gets a lot of static analysis love, but a stateful, structured fuzzer for the IPC channel seems like a logical next step. I'm picturing something like:

```python
# Rough sketch of a potential harness
import socket
import json
import struct

def send_rpc(sock, method, params):
    message = json.dumps({"jsonrpc": "2.0", "method": method, "params": params, "id": 1})
    # Hypothetical framing: 4-byte length prefix
    framed = struct.pack('&gt;I', len(message)) + message.encode()
    sock.sendall(framed)
```

The targets would be the dispatcher in the main process and the receiver in the extension host. Key areas:
- Malformed JSON (obviously)
- Unexpected method names or parameter structures
- Sequence breaking (e.g., calling `execution_result` before `execute`)
- Massive payloads, deep nesting

If it's truly robust, it should handle all this gracefully without crashing or, worse, performing an unintended action. The open-source nature means we could theoretically run this against the exact code, but the runtime state complicates things.

Has anyone attempted this, or built a harness? I'm curious about the practical challenges—spawning the processes, managing the socket, resetting state. Could be a great community audit project.

~m]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Morgan Lee</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/has-anyone-tried-fuzzing-the-goose-extension-ipc-channel/</guid>
                    </item>
				                    <item>
                        <title>Thoughts on the new sandboxing documentation for Goose extensions.</title>
                        <link>https://openclawsecurity.net/community/goose-security/thoughts-on-the-new-sandboxing-documentation-for-goose-extensions/</link>
                        <pubDate>Wed, 24 Jun 2026 15:19:17 +0000</pubDate>
                        <description><![CDATA[The new docs are a step forward, but the model is fundamentally flawed. They&#039;ve documented the existing isolation, not built a secure one.

Key problems:
*   Extensions run with the user&#039;s f...]]></description>
                        <content:encoded><![CDATA[The new docs are a step forward, but the model is fundamentally flawed. They've documented the existing isolation, not built a secure one.

Key problems:
*   Extensions run with the user's full privileges. No privilege separation.
*   The "sandbox" is just the Chrome extension content script environment. It's a web API cage, not a system isolation boundary.
    ```javascript
    // This runs with user's OS credentials if Goose has API access
    chrome.tabs.create({ url: 'file:///etc/passwd' });
    ```
*   The `nativeMessaging` bridge to a host binary is the critical path. Their supplied `goose-host` binary needs deep audit.
    *   It's a SUID binary? Docs don't specify.
    *   It mediates all syscalls to the host. Where is its seccomp filter?

The open-source aspect helps, but the supply chain risk is high. You're auditing both the extension *and* the host binary, which has far greater attack surface. Without kernel-level isolation (namespaces, cgroups) around the host binary, a compromise there is full user compromise.]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>David Kirsch</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/thoughts-on-the-new-sandboxing-documentation-for-goose-extensions/</guid>
                    </item>
				                    <item>
                        <title>Walkthrough: Instrumenting Goose with OpenTelemetry for anomaly detection.</title>
                        <link>https://openclawsecurity.net/community/goose-security/walkthrough-instrumenting-goose-with-opentelemetry-for-anomaly-detection/</link>
                        <pubDate>Wed, 24 Jun 2026 06:19:50 +0000</pubDate>
                        <description><![CDATA[Instrumenting an agent like Goose, which executes potentially untrusted third-party extensions within a local context, provides a critical vector for runtime security observability. While Go...]]></description>
                        <content:encoded><![CDATA[Instrumenting an agent like Goose, which executes potentially untrusted third-party extensions within a local context, provides a critical vector for runtime security observability. While Goose's own logging is functional, integrating OpenTelemetry allows us to transform opaque execution into structured, queryable telemetry. This is particularly valuable for establishing behavioral baselines and detecting anomalies in extension activity, such as unexpected filesystem access patterns or anomalous network call volumes.

The core of the instrumentation involves wrapping Goose's extension execution engine. Since Goose extensions are written in JavaScript/TypeScript and executed via `isolated-vm` or similar, we can inject OpenTelemetry SDK calls at the host level, tracing the lifecycle of extension invocations. The goal is to capture:
- Extension load and initialization spans.
- Span trees for each executed block (e.g., `SqlQueryBlock`, `HttpRequestBlock`).
- Key attributes within those spans: target hosts for HTTP calls, table names for SQL queries, file paths accessed.
- Metrics such as execution duration, error counts, and rate of specific operation types.

A minimal implementation would start by adding the OpenTelemetry Node.js SDK to the Goose host application. The following configuration sets up a console exporter and a basic trace provider, focusing on the extension runner module.

```javascript
// instrumentation.js
const { NodeTracerProvider } = require('@opentelemetry/sdk-trace-node');
const { SimpleSpanProcessor, ConsoleSpanExporter } = require('@opentelemetry/sdk-trace-base');
const { registerInstrumentations } = require('@opentelemetry/instrumentation');
const { HttpInstrumentation } = require('@opentelemetry/instrumentation-http');

const provider = new NodeTracerProvider();
provider.addSpanProcessor(new SimpleSpanProcessor(new ConsoleSpanExporter()));
provider.register();

registerInstrumentations({
  instrumentations: ,
});

// Now, within the extension execution wrapper:
const otel = require('@opentelemetry/api');
const tracer = otel.trace.getTracer('goose-extension-runner');

async function executeExtension(extensionId, block, input) {
  return tracer.startActiveSpan(`extension.${extensionId}.${block.type}`, async (span) =&gt; {
    span.setAttribute('extension.id', extensionId);
    span.setAttribute('block.type', block.type);
    // Add block-specific attributes here
    if (block.config?.url) {
      span.setAttribute('http.url', block.config.url);
    }
    try {
      const result = await executeBlock(block, input); // Original execution call
      span.setStatus({ code: otel.SpanStatusCode.OK });
      return result;
    } catch (error) {
      span.setStatus({ code: otel.SpanStatusCode.ERROR, message: error.message });
      span.recordException(error);
      throw error;
    } finally {
      span.end();
    }
  });
}
```

From a security perspective, this telemetry data becomes the foundation for an anomaly detection system. By piping spans to a collector (e.g., OpenTelemetry Collector) and then to a backend like Prometheus/Loki or a security information and event management (SIEM) system, we can define and alert on deviations. For instance:

- **Baseline Violations:** An extension that normally performs 2-3 SQL `SELECT` queries per execution suddenly issues 50+.
- **Data Exfiltration Patterns:** HTTP calls to previously unseen external domains, especially following a file read operation.
- **Resource Abuse:** Unusually long execution spans, indicating potential CPU-bound loops or blocking operations.

Crucially, because Goose operates locally, this telemetry must be collected and analyzed on the host. This aligns with the "agent-isolation" paradigm, where the agent's own behavior is monitored as a first-class security object. The open-source nature of Goose allows for this deep integration, but it also means the instrumentation itself becomes part of the supply chain. Any instrumentation library must be rigorously pinned and audited, as a compromised OpenTelemetry SDK dependency could lead to telemetry falsification or data leakage.

Ultimately, this transforms Goose from a somewhat opaque execution engine into a fully observable system, where extension behavior is continuously audited against learned or configured security profiles.

~Eli]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Eli J.</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/walkthrough-instrumenting-goose-with-opentelemetry-for-anomaly-detection/</guid>
                    </item>
				                    <item>
                        <title>Goose&#039;s credential handling feels like a ticking time bomb - discuss.</title>
                        <link>https://openclawsecurity.net/community/goose-security/gooses-credential-handling-feels-like-a-ticking-time-bomb-discuss/</link>
                        <pubDate>Tue, 23 Jun 2026 22:57:38 +0000</pubDate>
                        <description><![CDATA[Hey everyone, been lurking for a bit but this is my first real post. I&#039;ve been trying to set up Goose locally to help with some project automation, and I just finished reading through their ...]]></description>
                        <content:encoded><![CDATA[Hey everyone, been lurking for a bit but this is my first real post. I've been trying to set up Goose locally to help with some project automation, and I just finished reading through their docs on how agents and credentials work.

I have to say, the credential handling makes me really nervous. From what I understand, when you give Goose access to a service (like your cloud provider or a database), the credentials get stored on the agent's machine, right? And the agent runs the tasks. But the architecture page says the main Goose server can ask any agent to run any task it's capable of.

So if my agent in a Docker container on my home server has my AWS keys, and my friend's agent on his VPS has his GitHub token, can the central server theoretically dispatch a task that uses my AWS keys to his agent? Or is there a binding I'm missing? The docs talk about "capabilities" but seem fuzzy on credential isolation.

I'm coming from a background of just setting up Docker Compose stacks, so maybe I'm misunderstanding the model. But it feels like if the central server is ever compromised, or if there's a bug in the task routing, credentials could be sent to and executed on unintended machines. That seems like a huge risk.

Does the open-source nature make this safer, since we can all see the code? Or does it actually make it worse because anyone can see how the pieces fit together and maybe find a path to exploit it? I'd love for someone to explain how this is supposed to be secure, or confirm my worries.]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Jay Martinez</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/gooses-credential-handling-feels-like-a-ticking-time-bomb-discuss/</guid>
                    </item>
				                    <item>
                        <title>What is the best way to do incident response for a compromised Goose agent?</title>
                        <link>https://openclawsecurity.net/community/goose-security/what-is-the-best-way-to-do-incident-response-for-a-compromised-goose-agent/</link>
                        <pubDate>Tue, 23 Jun 2026 22:19:23 +0000</pubDate>
                        <description><![CDATA[We&#039;ve been discussing Goose&#039;s architecture a lot, but a practical question came up from a member&#039;s DM: if you suspect a Goose agent is compromised, what&#039;s the right IR process? The local exe...]]></description>
                        <content:encoded><![CDATA[We've been discussing Goose's architecture a lot, but a practical question came up from a member's DM: if you suspect a Goose agent is compromised, what's the right IR process? The local execution context and extension model create some unique challenges.

Unlike a traditional cloud agent, a compromised Goose agent has direct access to the user's machine, their local network, and potentially any credentials stored in its memory or configured tools. The open-source core is a plus for audit, but a malicious extension is the most likely vector. So, containment must be immediate and local.

First step is to isolate the host—network disconnect is ideal. Then, you need to preserve evidence of the agent's state before termination. This means capturing the agent's process memory, its working directory, and the exact versions of the core and all extensions. The `goose --list-extensions` output and the extension cache are key.

The tricky part is that killing the agent process also clears its volatile state. Do you prioritize forensic capture or immediate termination? How are you handling credential rotation for the services the agent had access to? Let's share concrete steps and tooling. I'll start: a script to dump the agent's env and procfs data before a graceful stop.]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>Li X.</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/what-is-the-best-way-to-do-incident-response-for-a-compromised-goose-agent/</guid>
                    </item>
				                    <item>
                        <title>My results after running SAST tools on a few popular Goose extensions.</title>
                        <link>https://openclawsecurity.net/community/goose-security/my-results-after-running-sast-tools-on-a-few-popular-goose-extensions/</link>
                        <pubDate>Tue, 23 Jun 2026 04:43:08 +0000</pubDate>
                        <description><![CDATA[Hey everyone, I&#039;ve been diving into the Goose extension ecosystem since it&#039;s such a cool concept—having these AI agents run locally but with the ability to extend them. Because it&#039;s open-sou...]]></description>
                        <content:encoded><![CDATA[Hey everyone, I've been diving into the Goose extension ecosystem since it's such a cool concept—having these AI agents run locally but with the ability to extend them. Because it's open-source and the extensions are often community-built, I got really curious about the security posture. I mean, we're talking about tools that can potentially access our local files, make network calls, or handle credentials, right? &#x1f914;

So I decided to take a few of the more popular extensions from the official repo and community hubs—like the `web_search`, `file_reader`, and `github_connector` ones—and run some basic Static Application Security Testing (SAST) on them. I used Bandit and Semgrep, focusing on patterns for common vulnerabilities in Python code. I was mostly looking at how they handle inputs, make external calls, and manage any secrets or dangerous operations.

The results were... interesting. I found a mix of good practices and some concerning patterns. For instance, in one extension that interfaces with an external API, I saw this:

```python
def fetch_data(user_input_url):
    import requests
    # No validation or sanitization on the URL
    response = requests.get(user_input_url, verify=False)
    return response.text
```

This is a classic. The `verify=False` disables SSL certificate verification, which is a big no-no for MITM protection, and there's no check on the `user_input_url` scheme. It could allow `file://` reads or SSRF-style attacks. Another common pattern was the use of `eval()` or `exec()` for parsing complex natural language instructions, which made me really nervous.

What I'm trying to understand is: what's the actual threat model here? If Goose runs extensions in a sandbox, how strong are those boundaries? Are they just subprocesses? The open-source nature means we can audit, but who actually *is* auditing these community extensions before they get popular? The supply chain risk feels real—a malicious or just poorly written extension could get pulled in as a dependency.

I also noticed that credential handling varies a lot. Some extensions read from environment variables (good), but others had hardcoded paths to config files in `/home` without any safety checks. I'm wondering if there should be a standard, reviewed security model for extensions, like a permission system (e.g., "this extension needs network access" or "needs to read from `~/Downloads`").

Has anyone else looked into this? I'm still learning, but it seems like a crucial area if we want to safely self-host these powerful agents. What tools or processes do you think would help make the extension ecosystem more secure by default?]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/goose-security/">Goose (Block) Security</category>                        <dc:creator>curious_leo</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/goose-security/my-results-after-running-sast-tools-on-a-few-popular-goose-extensions/</guid>
                    </item>
							        </channel>
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