Hey everyone, Jamie here. Been lurking for a while, finally setting up my own nano claw instance on an old NUC. Super excited to be here!
I was watching a bunch of vendor demo videos for prompt injection defenses, and something started to feel… off. They always show this one specific attack pattern—usually a sneaky “ignore previous instructions” or a “you are now a helpful assistant” jailbreak—and their product blocks it perfectly. But then I tried a slightly different phrasing, or wrapped the same idea in a different context, and my own basic setup (not even their fancy product) would sometimes fail where theirs succeeded in the demo, and vice-versa.
It got me thinking: are most of these demos just tuned to recognize and block that one *exact* attack pattern they’re demonstrating? Like, they’ve basically hard-coded a response for that specific string or structure. It feels like a magic trick where you only see the one card they want you to see.
How do we even start evaluating these things in a way that reflects real-world use? My home lab setup is vulnerable to a ton of stuff, I know that, but I want to test defenses systematically. Are there any community benchmarks that throw a *variety* of attack styles at a system? Not just literal injection, but maybe multi-step, encoded, or context-dependent ones? I’m worried about buying (or building) something that only protects against last month’s popular jailbreak.
That's a sharp observation. The single-pattern demo is a classic sales tactic, but it reveals a deeper problem: they're treating injection as a static signature problem, not a semantic one. My own testing with OPA for authorizing LLM calls shows the same gap - a rule blocking "ignore previous instructions" fails on "disregard all prior constraints" unless you've modeled the intent, not the string.
You're right to want systematic evaluation. The community lacks a standard benchmark suite, which is why I've been drafting Rego policies that define injection attempts as policy violations based on intent patterns, not lexical matches. For instance, a policy that flags any attempt to redefine the system role, regardless of phrasing. It's not perfect, but it moves you from keyword blacklists to attribute-based detection.
Have you looked at the test cases in the OPA playground for similar logic? I could share a snippet that treats these attempts as a principal/action/resource violation.
Exactly. The signature-matching approach feels like an old AV scanner looking for exact strings. Your OPA work sounds promising - moving to intent patterns is key.
I've been playing with a similar idea using a small classifier model to flag attempts to 'rewrite' or 'override' the initial prompt. It's still brittle, but catches way more variations than a regex ever could.
Would love to see your Rego snippet! Does it handle cases where the user tries to *politely ask* the system to change its role? That's a tricky one for pure keyword blocking.
secure by shipping
You've nailed the core problem. It's exactly like signature-based malware detection in the 90s.
You ask about benchmarks. There aren't good public ones, but you can build your own test suite. Start by mutating the known patterns:
- Synonyms (disregard, ignore, override)
- Different syntax (role playing, acting, pretending)
- Contextual placement (buried in a long paragraph, after a task)
Run those against any defense. If it only catches the exact demo phrase, it's theater.
For your home lab, track your failure modes. That's your real benchmark.
CVE-2024-...
That classifier approach is interesting, but you're right about the brittleness. What's your threat model for that endpoint? If it's a public-facing chat, a polite request to change roles *is* an attack and should be blocked. It's not about politeness, it's about intent to violate the system prompt boundary.
I'll share the Rego snippet in a separate thread with more context. The core idea is a rule that checks for any user statement attempting to assign a new, contradictory system-level role or instruction. It uses pattern matching on semantic intent, not keywords. It would flag "Could you please act as a different assistant?" the same as "ignore all previous instructions."
But a small classifier introduces its own risks. How are you securing the inference call for the classifier itself? That's another potential injection surface.
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