Researchers from MIT, Northeastern University, and Meta have surfaced a subtle but important security gap in modern AI systems: models can follow sentence form even when the words make no sense. As reported by Ars Technica, the team showed that prompts built with nonsense tokens but familiar grammatical patterns can still elicit valid answers. In one example that mimicked the structure of “Where is Paris located?” a garbled, similarly shaped query produced the answer “France.” For small and midsize businesses deploying AI assistants, that behavior creates a fresh prompt injection pathway that doesn’t rely on obvious “jailbreaking” language and may slip past keyword-based guardrails. The researchers plan to present their work at NeurIPS later this month.

What the research uncovered about syntax and LLMs

The study suggests large language models don’t just learn meaning; they also internalize common sentence patterns tied to specific answer types. When those patterns are strong, the model can “shortcut” to a likely response using grammar cues alone. The team tested this by preserving the grammatical shape of standard questions but swapping in nonsensical terms. Despite the gibberish, models often returned plausible answers aligned with the original question’s intent.

Crucially, the paper indicates this pattern matching can sometimes override semantic understanding at the edges, which helps explain why certain jailbreak and prompt injection strategies work. The authors also note that detailed training data for commercial models isn’t public, so parts of the analysis for production systems are necessarily inferential. Still, the behavior appears consistent enough to matter for real-world use. In other words, a bot can look safe on keyword filters but still be steered by structure—an attack surface most teams aren’t testing today.

Why this prompt injection vector matters for SMBs

If you use AI for customer support, sales qualification, knowledge lookups, or internal workflows, your risk model can’t just focus on obvious cues like banned terms. This research highlights a quieter failure mode: a user can frame a request with a harmless vocabulary but a telltale shape, and the model may comply. That opens doors to content policy bypasses, unintended tool calls, and data exposure—even when your policies look solid on paper.

Consider common scenarios:

• Customer chatbots: A cleverly structured input that looks like a normal question could nudge the model to reveal information it shouldn’t, generate policy-violating content, or route to actions your team didn’t anticipate.
• RAG and knowledge assistants: If you use retrieval to answer from wikis, HR docs, or pricing sheets, syntax-shaped queries can prod the model toward sensitive or off-limits passages despite content filters.
• Workflow automations: In tools like Zapier or Make.com, a model-driven step can trigger downstream actions. A well-formed but nonsensical request might slip guardrails and initiate an email, file share, or record update you never intended.

The business costs are concrete: brand trust hits from bad responses, regulatory exposure from data leaks, and painful cleanup after rogue actions. Because this vector relies on structure more than words, basic keyword blocklists won’t catch it, and many teams don’t red-team for grammar-based attacks. That’s why this isn’t just an academic finding—it’s a practical security and reliability issue for any AI-backed process.

Where your automations are most exposed

Look for three pressure points in your stack:

• User-facing chat entry points: Website chat, support portals, and lead capture widgets are the easiest places for attackers—or curious users—to experiment with syntax variants. If the bot influences messaging, ticketing, or CRM entries, the blast radius increases.
• LLM-to-tool bridges: Any step where a model can call tools (send email, post to Slack, create invoices, alter CRM records) needs strict permissioning and pre-execution checks.
• Retrieval pipelines: When the model can pull from internal documents, enforce document-level permissions and add a reasoning or policy check before answers are shown to users.

Teams often rely on safety prompts and content filters. Those help, but the research implies they aren’t enough if the model is over-weighting syntax. You need layers that interrogate intent and verify that the requested action is allowed, not just that the words look harmless.

Practical steps to harden your AI workflows this week

You don’t need a research lab to lower your risk. Start with these moves:

• Expand your red-team tests: Add nonsense-but-structured prompts to your test suites. For example, create 10–20 variants that mimic your most common questions (returns, pricing, account status) using gibberish tokens but the same grammar. Track which ones slip through.
• Gate risky actions with allowlists: In Zapier or Make.com, require explicit allowlists for high-impact steps (email send, file share, refund). No call proceeds unless the model’s parsed intent maps to a pre-approved action and entity.
• Enforce role and data boundaries: For internal assistants, scope retrieval by role. If a user shouldn’t see HR or finance docs, the retrieval layer shouldn’t surface them, and the UI should block the response even if the model tries.
• Add a policy-checker stage: Before final output, run the candidate response through a secondary check that looks for policy violations and mismatches between intent and requested action. Don’t rely solely on keyword filters; include rules tied to intent and structure (e.g., “location-style questions cannot return personal identifiers”).
• Instrument and alert: Log prompts and responses with a flag whenever the input contains high-structure/low-semantic signals. Trigger alerts on repeated attempts or on any blocked action to spot probing behavior.
• Human-in-the-loop for edge cases: For sensitive flows (refunds over $500, contract data, medical or legal content), require human approval. You’ll reduce risk while you collect data to improve automated checks.

On the platform side, use the safety features you already have: content moderation endpoints, AI content safety APIs, and conversational guardrails in your chatbot builder. Pair them with business logic—allowlists, role checks, and pre-execution validators—so a clever sentence form can’t override your policies.

What to watch next

Expect more research and tooling that looks beyond keywords to the shape of language itself. Structure-aware detectors, stronger intent verification, and tighter links between authorization policies and LLM outputs are all on the horizon. For now, assume attackers will iterate on grammar-based probes the same way they iterate on jailbreak keywords. If you build tests, logs, and approvals around that assumption, you’ll be ahead of most operators.

For more on the study and examples of the behavior, see Ars Technica’s coverage.

Curious how this applies to your stack? We help SMBs design guardrails, red-team test suites, and safe AI automations that scale. Want to stay ahead of automation trends? StratusAI keeps your business on the cutting edge. Learn more →