Large Language Models (LLMs) bring incredible capabilities — but they’re also vulnerable to prompt-based adversarial attacks, where carefully crafted inputs manipulate the model into breaking rules or leaking sensitive information.

⚠️ Common Prompt Attack Methods

  • Prompt Injection — Insert malicious instructions into user input, tricking the model into following attacker goals.

  • Jailbreaking— Craft prompts that bypass safeguards (e.g., role-play exploits like the “grandma story”), leading to inappropriate or unsafe outputs.

  • Prompt Override — Inject competing objectives that override system instructions, sometimes causing denial-of-service in multi-turn contexts.
  • Indirect Injection — Hide adversarial prompts inside web pages or documents that the LLM ingests.
  • Data Exfiltration — Extract memorized sensitive data from model responses or chat history.
  • Impersonation — Instruct the LLM to adopt a new identity and ignore prior safety rules.

🛡️ Defenses & Mitigations

Securing against prompt attacks is a shared responsibility between LLM platforms and application builders.

LLM Provider Controls:

  • Content filtering, red-teaming, and regular safety updates.
  • Privacy-preserving training practices to minimize memorization.

Application-Level Controls:

  • Input sanitation & validation before sending prompts.
  • Prompt engineering to enforce boundaries and context.
  • Moderation APIs (e.g., OpenAI, Hugging Face) for real-time filtering.
  • Secure output encoding to prevent code execution or injection.
  • Guardrails frameworks (e.g., NVIDIA NeMo Guardrails, WhyLabs, Robust Intelligence) for advanced input/output filtering.

💬 Question for you: Have you tested your LLM applications for prompt injection risks? If yes, what defenses worked best? #AISecurity #PromptInjection #LLMSecurity #AdversarialML #Cybersecurity