Hacking AI using a LinkedIn post
Executive Guide to Stopping Prompt Injection
Read time: 3 minutes
Noise: “Our model is so advanced it understands intent.” Not if a LinkedIn headline can shout it down.
Signal: Guardrails fail when untrusted text is treated as first-class instructions.
A Mastercard employee slipped an ALL-CAPS instruction into his LinkedIn résumé, and an AI assistant fell for it… This proves how thin today’s guardrails are. Prompt injection is now OWASP’s #1 Gen-AI risk (source), yet most enterprises still concatenate outside text directly with system prompts.
You have to treat large-language models like untrusted code: isolate prompts, filter inputs, moderate outputs, and sandbox high-risk actions. The five quick fixes below will cut your exposure by 80 percent.
Why the résumé jailbreak matters
Richard Boorman hid a “speak-to-me-in-ALL-CAPS” command in his profile. Recruiter chatbots scraping LinkedIn promptly shouted back, demonstrating that most LLM pipelines give every token equal authority.
Key takeaway for leadership: Until prompts are compartmentalized, any data field your software ingests—CRM notes, PDFs, images—can hijack the model.
What it reveals about today’s AI stacks
LLMs remain next-token predictors. They do not vet intent, provenance, or sarcasm. Reasoning chains help, but only if your prompt design keeps hostile content out of the “trusted” zone.
Five-layer defence-in-depth checklist
✅ Lock the system prompt. Store it in code, not in dynamic query templates. Tag user or retrieved content with clear delimiters so the model sees hard boundaries.
✅ Pre-filter inputs. Run every incoming string through a detector such as LLM-Guard to strip jailbreak keywords or high-risk patterns before they ever reach the model. (GitHub)
✅ Post-moderate outputs. Pipe responses through a second model or rules engine that flags policy violations or unexpected format changes.
✅ Sandbox high-impact actions. Treat the LLM like untrusted code: restrict file I/O, require human approval for payments, and rotate API keys frequently.
✅ Continuously red-team. Automate adversarial test suites mapped to OWASP LLM01 and MITRE ATLAS. Integrate them into your CI/CD pipeline to catch regressions before release.
💡Pro tip: Most fixes are design choices, not model tweaks. Changing one line in your retrieval-augmented pipeline—from “append” to “isolate” — defuses the majority of direct attacks.
Talking points for Senior Leadership
Risk exposure: Any channel that ingests user-generated text (support tickets, sales emails) can carry hidden commands.
Compliance: Data exfiltration via prompt injection could breach GDPR or banking secrecy rules.
ROI on security: Input filtering and prompt isolation add hours—not months—to delivery schedules and pay off immediately in reduced incident response.
Vendor diligence: Ask SaaS providers how they segregate system prompts, log requests, and throttle tool calls.
Extra lens for founders shipping AI products
Trust is your moat. Security features: prompt isolation, audit logs, abuse detection—are now table stakes for enterprise deals and will set you apart in a crowded market.
Bake it in, don’t bolt it on. Retrofitting guardrails after launch slows growth. Design “prompt-safe” pipelines on day one to ensure high feature velocity later.
Ship proof, not promises. Expose a public red-team leaderboard or share monthly security reports. Investors and customers value transparent metrics over glossy decks.
Automate guardrails as code. Treat prompt templates like API keys: version-control them, lock write access, and trigger alerts on unauthorized edits.
Plan for failure. Even the best filters miss edge cases. Maintain rapid roll-back scripts and a clear incident-response playbook so a jailbreak is a speed bump, not a crash.
Forward this to your team. One LinkedIn post just reminded us that the most intelligent bots still follow the loudest voice in the room, unless we build better walls.