Just now The Information broke a story that a major retailer’s newly launched chatbot was jailbroken into discussing off-limits topics after a guardrail misconfiguration
This is a difficult problem and I understand how stressful it can be for the teams involved.
Here is why most of Jailbreaks happen:
If you ask a single LLM to generate creative answers and simultaneously ask that same LLM to police itself, you are relying on probabilistic safety. You are essentially asking the model to “please be nice.”
In the face of adversarial attacks or complex social engineering, “soft” guardrails will bend.
When we started Alhena, this is the exact problem we decided to prioritize before we built anything else. This stuff is too important to be left to be `configured`, we decided it has to be working Out of Box.
We anticipated that single-model safety wouldn’t be enough for the enterprise.
Instead, we built a three-layered security architecture:
1️⃣ Independent Policy Agent: Enforces business rules separate from the answering model.
2️⃣ Independent Hallucination-Checker : Fact-checks and validates responses before the user sees them.
3️⃣ Malicious User Identifier: Analyzes user questions in real-time to identify malicious intent, and blocks them
We intentionally decouple the “brain” that generates the text from the “brain(s)” that approve it.
Did this cost us engineering time?
Yes.
Did we have to trade a few percentage points of deflection for absolute brand safety?
Yes.
Was it worth it for predictable, brand-safe behavior?
Absolutely.
AI agents are no longer fun experiments. They are digital employees wearing your logo. If they can be tricked, your brand reputation is what breaks, not just the code.
If a dedicated bad actor spent 30 minutes attacking your current AI assistant today, would it hold the line, or would it end up on the front page of The Information?
#LLMSecurity #enterpriseai #hallucination