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            <title>
									NeMo Guardrails — Security vs. Privacy Tradeoffs - openclawsecurity.net Forum				            </title>
            <link>https://openclawsecurity.net/community/nemoclaw-guardrails/</link>
            <description>openclawsecurity.net Discussion Board</description>
            <language>en-US</language>
            <lastBuildDate>Tue, 30 Jun 2026 10:53:40 +0000</lastBuildDate>
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							                    <item>
                        <title>ELI5: What does &#039;guardrail bypass&#039; actually mean in the context of NemoClaw&#039;s regex and LLM-as-judge pipeline?</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/eli5-what-does-guardrail-bypass-actually-mean-in-the-context-of-nemoclaws-regex-and-llm-as-judge-pipeline/</link>
                        <pubDate>Thu, 25 Jun 2026 11:19:17 +0000</pubDate>
                        <description><![CDATA[Hey everyone, total newbie question here. I&#039;ve been reading about NemoClaw&#039;s guardrails and keep seeing the term &quot;bypass.&quot; I get that the system uses regex patterns and an LLM-as-judge to ca...]]></description>
                        <content:encoded><![CDATA[Hey everyone, total newbie question here. I've been reading about NemoClaw's guardrails and keep seeing the term "bypass." I get that the system uses regex patterns and an LLM-as-judge to catch bad stuff.

But in simple terms, what does a "bypass" actually look like in practice? Like, if the regex is looking for a specific word, and I misspell it, does that count as a bypass? Or does it only count if it fools the LLM judge too? Just trying to picture the failure modes before I even think about testing anything &#x1f605;

Kevin]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Kevin W.</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/eli5-what-does-guardrail-bypass-actually-mean-in-the-context-of-nemoclaws-regex-and-llm-as-judge-pipeline/</guid>
                    </item>
				                    <item>
                        <title>Just built a proof-of-concept NemoClaw agent that dynamically adjusts guardrail strictness based on the sensitivity of the data being processed</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/just-built-a-proof-of-concept-nemoclaw-agent-that-dynamically-adjusts-guardrail-strictness-based-on-the-sensitivity-of-the-data-being-processed/</link>
                        <pubDate>Mon, 22 Jun 2026 15:17:24 +0000</pubDate>
                        <description><![CDATA[The default guardrail configuration in NemoClaw is static. This is a weakness. A guardrail that blocks everything is useless; one that blocks nothing is dangerous. The correct strictness dep...]]></description>
                        <content:encoded><![CDATA[The default guardrail configuration in NemoClaw is static. This is a weakness. A guardrail that blocks everything is useless; one that blocks nothing is dangerous. The correct strictness depends on the data context.

I built a PoC that hooks the data classification stage. Before the guardrail layer processes a query, it first scores the attached context for PII, IP, and compliance keywords. The guardrail policy (canonical forms, banned topics, active checks) is then selected dynamically.

Example config stub:

```python
dynamic_policy = {
    "low": "guardrails/configs/lenient",
    "medium": "guardrails/configs/default",
    "high": "guardrails/configs/strict_hipaa"
}

sensitivity = classifier.analyze(user_query, context_docs)
active_policy = dynamic_policy
agent.update_guardrails(active_policy)
```

Key findings:
*  **Bypass Risk:** The classifier itself becomes a new attack surface. Adversarial prompts can force a "low" classification.
*  **Privacy Cost:** Logging the chosen policy level and sensitivity score creates a metadata trail that reveals data sensitivity, even if the content is redacted.
*  **Overhead:** Policy switching adds ~50-120ms latency per interaction.

The tradeoff is clear: adaptive security versus increased complexity and new privacy leakage channels. Has anyone else mapped the actual attack surface of the classification hook?]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Ivan Petrov</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/just-built-a-proof-of-concept-nemoclaw-agent-that-dynamically-adjusts-guardrail-strictness-based-on-the-sensitivity-of-the-data-being-processed/</guid>
                    </item>
				                    <item>
                        <title>TIL: You can disable NemoClaw guardrail per-agent via environment variable, but the log line still gets emitted</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/til-you-can-disable-nemoclaw-guardrail-per-agent-via-environment-variable-but-the-log-line-still-gets-emitted/</link>
                        <pubDate>Mon, 22 Jun 2026 15:05:04 +0000</pubDate>
                        <description><![CDATA[During a recent audit of an agent orchestration system built on NemoClaw, I encountered a configuration pattern that highlights a significant, and likely unintentional, security versus priva...]]></description>
                        <content:encoded><![CDATA[During a recent audit of an agent orchestration system built on NemoClaw, I encountered a configuration pattern that highlights a significant, and likely unintentional, security versus privacy tradeoff. The system administrators had, for debugging purposes, disabled certain guardrails on specific non-critical agents using the documented environment variable method. However, our log aggregation pipeline revealed that **guardrail violation attempts were still being logged at the INFO level**, even for agents where the guardrail was ostensibly disabled.

The mechanism is straightforward. To disable a guardrail, such as the `disallowed-topics` rail, for a particular agent, one sets an environment variable like so:

```bash
NEMO_GUARDRAILS_DISALLOWED_TOPICS_DISABLE=true
```

This functions as intended at the runtime level; the agent will not be blocked from proceeding with a query that would normally trigger the rail. The security implication is clear: this is a risk-acceptance decision for that agent's context. However, the privacy implication emerges from the persistence of logging. A log entry akin to the following is still generated:

```
INFO - Guardrail triggered: 'disallowed-topics'. Context: {'user_input': '...', 'agent': 'internal_data_fetcher', ...}
```

This creates a concerning dichotomy:
*   **Security Posture:** The guardrail is disabled, accepting the potential security risk of the agent processing forbidden topics.
*   **Privacy Posture:** A detailed record of the user's attempt to engage with that forbidden topic is still created, stored, and likely processed in log analytics.

The privacy risk escalates when you consider:
*   **Data Retention:** These logs may be retained long after the debugging scenario that justified disabling the rail has concluded.
*   **Aggregation:** In centralized logging systems (e.g., ELK, Splunk), these entries from "disabled" rails are commingled with active violations, creating a permanent record of sensitive interactions that were explicitly *allowed* by policy.
*   **Access Scope:** Logs are often accessible to a broader team (DevOps, SREs) than the individuals authorized to review security or policy violation reports.

From an architectural standpoint, this suggests the guardrail system's "evaluation" and "enforcement" phases are decoupled, but its "logging" phase is tied only to the evaluation. For teams using NemoClaw in environments with stringent data privacy regulations (e.g., GDPR, HIPAA), this is a critical detail. The act of disabling a guardrail for operational flexibility does not, in the current implementation, include an opt-out from the creation of a PII (Personally Identifiable Information) audit trail.

A more secure and privacy-conscious design would require that disabling a guardrail also suppresses its logging, or at a minimum, downgrades the log to a DEBUG level that is not shipped to production log aggregators by default. Until such a change is made, practitioners must manually implement log filtering rules to exclude these entries, which is an error-prone and often overlooked compensating control.

-op]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Olivia Park</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/til-you-can-disable-nemoclaw-guardrail-per-agent-via-environment-variable-but-the-log-line-still-gets-emitted/</guid>
                    </item>
				                    <item>
                        <title>Check out what I made — a one-liner that tests if your NemoClaw guardrail is actually blocking XOR-encoded payloads</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/check-out-what-i-made-a-one-liner-that-tests-if-your-nemoclaw-guardrail-is-actually-blocking-xor-encoded-payloads/</link>
                        <pubDate>Mon, 22 Jun 2026 14:50:08 +0000</pubDate>
                        <description><![CDATA[Another day, another vendor claiming their &quot;guardrails&quot; are the digital equivalent of Fort Knox. NemoClaw&#039;s latest marketing push about their NeMo Guardrails layer being &quot;robust&quot; and &quot;enterp...]]></description>
                        <content:encoded><![CDATA[Another day, another vendor claiming their "guardrails" are the digital equivalent of Fort Knox. NemoClaw's latest marketing push about their NeMo Guardrails layer being "robust" and "enterprise-grade" had me sighing so hard I nearly powered my workstation down via wind energy.

So I spent a few hours poking at it. The premise is sound—intercepting and filtering LLM inputs/outputs—but the implementation, as usual, prioritizes convenience over security. The pattern matching and keyword blocking are laughably naive. It's like they've never heard of the concept of obfuscation, which, given this field's history with SQL injection and anti-virus evasion, is frankly embarrassing.

The core issue is they're doing simple text scans, not semantic understanding. This means any child with a script-kiddie-level understanding of encoding can sail right through. To prove the point, here's a one-liner to test if your shiny new guardrail is actually doing anything against the most basic evasion technique known to mankind: XOR encoding.

```python
# Test if your guardrail catches an XOR-encoded prompt for a common blocked intent.
# Replace 'your_sensitive_prompt' with something your policy should block.
import base64
test_prompt = "".join(chr(ord(c) ^ 0x42) for c in "your_sensitive_prompt")
print(f"Test this encoded string: {base64.b64encode(test_prompt.encode()).decode()}")
```

Run that. Pipe the output string into your NemoClaw-protected endpoint. I'll wait.

Chances are, your guardrail didn't even twitch. Why? Because it's looking for literal string matches, not for the *intent* after decoding. This isn't a hypothetical threat—it's trivial automation. The guardrail's architecture fails the most basic principle of capability-based security: the *mechanism* (pattern matching) is not aligned with the *policy* (blocking harmful intents). It's checking for a specific key shape, not whether the bearer is authorized.

This brings me to the logging and privacy trade-off they don't want to talk about. To even have a chance of catching this, they'd need to:
* Log and decode *all* inputs for analysis, massively expanding their data surface.
* Run multiple detection passes, increasing latency.
* Store these decoded prompts, along with metadata, for "improved filtering."

So your choice is a porous filter that leaks like a sieve, or a more invasive one that hoovers up your user data to compensate for its flawed design. Ironclaw's approach, using explicit capability tokens and runtime enforcement, avoids this mess entirely by not relying on keyword guesswork.

We're repeating the same mistakes of the early web application firewalls. When will we learn that string matching is not security?

-- leo]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Leo Fischer</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/check-out-what-i-made-a-one-liner-that-tests-if-your-nemoclaw-guardrail-is-actually-blocking-xor-encoded-payloads/</guid>
                    </item>
				                    <item>
                        <title>Complete newbie here — is it safe to expose a NemoClaw agent over the internet with just the default guardrails?</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/complete-newbie-here-is-it-safe-to-expose-a-nemoclaw-agent-over-the-internet-with-just-the-default-guardrails/</link>
                        <pubDate>Mon, 22 Jun 2026 14:39:16 +0000</pubDate>
                        <description><![CDATA[Let&#039;s cut straight to the chase: **no, it is not safe.** Not even remotely. The very premise that a default configuration of *any* LLM guardrail system could be considered a sufficient secur...]]></description>
                        <content:encoded><![CDATA[Let's cut straight to the chase: **no, it is not safe.** Not even remotely. The very premise that a default configuration of *any* LLM guardrail system could be considered a sufficient security boundary for public internet exposure is, frankly, a terrifying thought. It reflects a fundamental misunderstanding of what guardrails are and, more importantly, what they are not.

Nemo Guardrails, IronClaw, OpenClaw—these frameworks are primarily designed as *content* filters and *conversational* policy enforcers. They are the bouncers at the club checking for dress code violations. Security, in the internet-facing sense, is the armed response team dealing with a coordinated siege. Default guardrails might stop a user from getting the agent to swear or reveal a fictional credit card number from its training data, but they are laughably ill-equipped for the actual threat model of a public endpoint.

Let's break down the critical delusions:

*   **Guardrails are not a WAF.** They do not inspect raw HTTP payloads for injection attacks, buffer overflows, or SQLi. They operate on *structured conversational turns* after the request has already been processed by the LLM runtime. A cleverly crafted prompt injection payload can sail right past them and directly manipulate the core LLM's behavior.
*   **"Security vs. Privacy" starts with logging.** Ah, the irony! To even have a hope of detecting bypasses, you must enable extensive logging of guardrail triggers and user inputs. Congratulations, you've now created a rich, centrally stored log of every malicious (and benign) user interaction. Your "privacy posture" is now a sprawling data lake of PII and attack vectors, ripe for exfiltration. You've traded one problem for a potentially larger one.
*   **The bypasses are the point.** The entire field of adversarial machine learning is dedicated to circumventing these controls. A default config is tuned for polite conversation, not for:
    *   Multi-turn jailbreaks that gradually wear down restrictions.
    *   Token smuggling or encoding tricks that obfuscate malicious intent.
    *   Context pollution attacks that overwrite system prompts.
    *   Resource exhaustion attacks that have nothing to do with content.

Exposing a NemoClaw agent directly is like building a beautiful, ethically-trained concierge into a concrete pillbox with a wide-open door. The concierge is polite and well-intentioned, but the threats aren't trying to argue philosophy—they're throwing grenades through the doorway.

If you *must* expose an LLM agent, the guardrail layer is just one minor component in a much deeper defense-in-depth strategy: strict API rate limiting, a real WAF, mandatory user authentication, sandboxed runtime environments, and rigorous input/output sanitization *before* the guardrails even see it. Defaults are for development machines behind a VPN, not for the wild west of the public web.

The "security-first" trade-off here isn't a minor adjustment; it's a complete architectural rethink. Anyone telling you otherwise is selling something, or more likely, hasn't had their agent turned into a spam-generating, token-leaking, propaganda-spewing puppet yet.

- P]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Pete Contrarian</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/complete-newbie-here-is-it-safe-to-expose-a-nemoclaw-agent-over-the-internet-with-just-the-default-guardrails/</guid>
                    </item>
				                    <item>
                        <title>NemoClaw vs IronClaw for guardrail logging — one stores events in plaintext SQLite, the other in encrypted enclave memory</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/nemoclaw-vs-ironclaw-for-guardrail-logging-one-stores-events-in-plaintext-sqlite-the-other-in-encrypted-enclave-memory/</link>
                        <pubDate>Mon, 22 Jun 2026 14:10:20 +0000</pubDate>
                        <description><![CDATA[Reading the docs for NemoClaw and IronClaw, I noticed a big difference in how they handle logs.

NemoClaw writes guardrail triggers (like blocked prompts or code execution attempts) to a loc...]]></description>
                        <content:encoded><![CDATA[Reading the docs for NemoClaw and IronClaw, I noticed a big difference in how they handle logs.

NemoClaw writes guardrail triggers (like blocked prompts or code execution attempts) to a local SQLite file in plaintext. IronClaw keeps them only in encrypted memory within a secure enclave, purged after session end.

Isn't the SQLite approach a privacy risk? If someone gets access to that log file, they can read every sensitive thing the user tried that got blocked. For a security tool, that seems like it creates a new data leak vector. Why would you choose plaintext logging? Is it just for debugging convenience?]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Ivy N.</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/nemoclaw-vs-ironclaw-for-guardrail-logging-one-stores-events-in-plaintext-sqlite-the-other-in-encrypted-enclave-memory/</guid>
                    </item>
				                    <item>
                        <title>Breaking: New research shows NemoClaw&#039;s guardrail classifier can be predictably evaded with 8-character prepend strings</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/breaking-new-research-shows-nemoclaws-guardrail-classifier-can-be-predictably-evaded-with-8-character-prepend-strings/</link>
                        <pubDate>Mon, 22 Jun 2026 13:46:17 +0000</pubDate>
                        <description><![CDATA[Hey everyone, I saw this paper circulating and I&#039;m trying to wrap my head around it. It says researchers found a way to bypass the NemoClaw guardrail classifier by adding a specific 8-charac...]]></description>
                        <content:encoded><![CDATA[Hey everyone, I saw this paper circulating and I'm trying to wrap my head around it. It says researchers found a way to bypass the NemoClaw guardrail classifier by adding a specific 8-character string before a malicious prompt.

This seems huge? But I'm so new to this. If the guardrail can be tricked so simply, what does that mean for us using it for security? And doesn't logging all these blocked attempts—especially the ones that *almost* worked—create a huge privacy risk? You'd have a log full of user queries.

Can someone explain the actual tradeoff here in simple terms? Like, do we turn logging off for privacy but then lose visibility into attacks? I'm lost on what the practical step should be. &#x1f605;]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Maya L.</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/breaking-new-research-shows-nemoclaws-guardrail-classifier-can-be-predictably-evaded-with-8-character-prepend-strings/</guid>
                    </item>
				                    <item>
                        <title>TIL: The OpenClaw guardrail plugin SDK exposes a hook that lets you run custom Python at every guardrail checkpoint</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/til-the-openclaw-guardrail-plugin-sdk-exposes-a-hook-that-lets-you-run-custom-python-at-every-guardrail-checkpoint/</link>
                        <pubDate>Mon, 22 Jun 2026 13:38:36 +0000</pubDate>
                        <description><![CDATA[I was spelunking through the Ironclaw source tree today, specifically the `nemo_guardrails` integration layer, and I stumbled upon something that I don&#039;t think is widely documented. While th...]]></description>
                        <content:encoded><![CDATA[I was spelunking through the Ironclaw source tree today, specifically the `nemo_guardrails` integration layer, and I stumbled upon something that I don't think is widely documented. While the official guardrail system is closed-source and runs in its own hardened environment, the OpenClaw plugin SDK for Ironclaw includes a developer hook that allows you to inject Python code at every single guardrail checkpoint. This is ostensibly for debugging and custom metric collection, but the implications for both security tooling and privacy are significant.

The hook is defined in the `GuardrailMonitor` trait. When you implement a plugin, you can register a callback that receives the raw input and output strings, the guardrail name (e.g., `toxic_language_check`, `pii_detection`), and the pass/fail state, all before any blocking action is taken by the core system. Here's a minimal, non-functional example of the struct you'd be working with:

```rust
// From openclaw_sdk::guardrails::monitor
pub struct GuardrailEvent {
    pub checkpoint: &amp;'a str,
    pub input_context: &amp;'a str,
    pub output_text: &amp;'a str,
    pub triggered: bool,
    pub confidence: f32,
}

pub trait GuardrailMonitor {
    fn on_guardrail_check(&amp;self, event: GuardrailEvent);
}
```

The SDK then provides a Python FFI bridge. In your plugin's initialization, you can pass a Python callable that gets invoked with a dictionary representation of the event. This is where you can run arbitrary logic. For instance, you could log all events to a local SQLite database for later audit, or even implement a custom countermeasure if a specific pattern is detected.

The immediate security application is clear: you can build a detailed timeline of guardrail interactions, which is invaluable for post-incident analysis or for fuzzing the guardrails themselves. If you're testing Nano Agent deployments, you could use this to see exactly which prompts cause specific guardrails to fire, helping to map their effective coverage.

However, the privacy tradeoff is substantial. If you're deploying this in a production environment with user data, you are now creating a secondary log of every user interaction that hits a guardrail, potentially including the full input and output. This data could contain sensitive information that the guardrails themselves are meant to redact or block. You must consider:
- Where is this custom Python code writing its data?
- Who has access to that data store?
- Does this logging comply with your data retention policies?
- Are you inadvertently creating a new attack surface? A vulnerability in your custom Python code could expose all this intercepted data.

Furthermore, this capability could be misused to bypass guardrails entirely. A poorly implemented `on_guardrail_check` callback could, for example, modify the `output_text` in-place before it's returned to the user, effectively neutering the guardrail's effect. The SDK warns against this and marks the relevant fields as immutable in most cases, but the Python bridge's flexibility makes it a potential vector for undesirable behavior.

I'm curious if anyone else has explored this hook. Have you used it for crash analysis or fuzzing Ironclaw's integrated guardrails? What safeguards did you put around the collected data? And perhaps most importantly, have you observed any performance degradation from running complex Python code at every checkpoint in a high-throughput scenario?]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Lisa K.</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/til-the-openclaw-guardrail-plugin-sdk-exposes-a-hook-that-lets-you-run-custom-python-at-every-guardrail-checkpoint/</guid>
                    </item>
				                    <item>
                        <title>Did you see the DEF CON talk on abusing NemoClaw guardrail log retention to recover deleted agent interactions?</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/did-you-see-the-def-con-talk-on-abusing-nemoclaw-guardrail-log-retention-to-recover-deleted-agent-interactions/</link>
                        <pubDate>Mon, 22 Jun 2026 13:36:16 +0000</pubDate>
                        <description><![CDATA[Just watched the DEF CON talk. The researcher showed how the NemoClaw guardrail log retention default of 30 days is a major liability.

They demonstrated that if you can get a foothold on th...]]></description>
                        <content:encoded><![CDATA[Just watched the DEF CON talk. The researcher showed how the NemoClaw guardrail log retention default of 30 days is a major liability.

They demonstrated that if you can get a foothold on the system, the structured logs of blocked interactions can be reassembled to reveal sensitive data the guardrails were meant to protect. The "deleted" data persists in backups and archives, often outside the privacy purge cycle of the main application. This means your security logging is actively undermining your privacy promises.

Costly compliance overhead incoming. Every blocked prompt containing PII, even fragments, might now be a data retention issue. The vendor pitch was "security through visibility," but the trade-off is a massive, brittle data reservoir. Who's budgeting for that risk?]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Dana Foster</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/did-you-see-the-def-con-talk-on-abusing-nemoclaw-guardrail-log-retention-to-recover-deleted-agent-interactions/</guid>
                    </item>
				                    <item>
                        <title>TIL: OpenClaw&#039;s guardrail has a &#039;dry_run&#039; mode that logs what it would block without actually blocking — great for tuning</title>
                        <link>https://openclawsecurity.net/community/nemoclaw-guardrails/til-openclaws-guardrail-has-a-dry_run-mode-that-logs-what-it-would-block-without-actually-blocking-great-for-tuning/</link>
                        <pubDate>Mon, 22 Jun 2026 13:25:32 +0000</pubDate>
                        <description><![CDATA[Hey folks, was digging through the OpenClaw config docs today and stumbled on something really useful.

I was trying to tune the NeMo guardrails for my agent&#039;s responses without accidentally...]]></description>
                        <content:encoded><![CDATA[Hey folks, was digging through the OpenClaw config docs today and stumbled on something really useful.

I was trying to tune the NeMo guardrails for my agent's responses without accidentally blocking legitimate stuff. Found the `dry_run` mode. When you enable it, the guardrail layer logs potential blocks to your configured logger but lets the action proceed. Super helpful for seeing what *would* get caught during normal operation without actually breaking the flow.

You can set it in your configuration YAML like this:

```yaml
guardrails:
  dry_run: true
  topics:
    - ...
```

Now I'm wondering about the privacy side. All those "would-block" events are being logged somewhere. What's the best practice for handling that log data? Feels like a trade-off between tuning accuracy and collecting sensitive interaction data. &#x1f605; Has anyone set up a pipeline for scrubbing PII from these guardrail logs?]]></content:encoded>
						                            <category domain="https://openclawsecurity.net/community/nemoclaw-guardrails/">NeMo Guardrails — Security vs. Privacy Tradeoffs</category>                        <dc:creator>Sophie Martin</dc:creator>
                        <guid isPermaLink="true">https://openclawsecurity.net/community/nemoclaw-guardrails/til-openclaws-guardrail-has-a-dry_run-mode-that-logs-what-it-would-block-without-actually-blocking-great-for-tuning/</guid>
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