Did you know that “prompt attacks” are one of the biggest sources of risk in AI?

Large Language Models (LLMs) have transformed productivity, but they are not immune to manipulation. Their flexibility is, at the same time, their most vulnerable point. If your company is integrating AI solutions, understanding prompt attacks is not an option—it’s an urgent necessity.

LLMs can be forced to bypass internal rules, leak sensitive data, or return biased results that directly impact your operation or client security.

What Exactly is a Prompt Attack?

A prompt attack is any technique that uses the input text (the prompt or the content the model processes) to force the model to behave in an unwanted, malicious, or unauthorized way.

It’s not just about getting the model to say something inappropriate; it’s about breaking its safeguards or using it as an unwitting tool for cybercrime.

The 12 Most Common Prompt Risks (Clearly Explained)

To help your team identify these risks, here are the 12 most frequent types of prompt attacks today:

  • Jailbreaks: Essentially, tricking the model into ignoring its security instructions and internal policies, freeing it to perform prohibited tasks.
  • Prompt Injection: Inserting malicious or contradictory instructions within texts, documents, URLs, or databases that the model is designed to process.
  • Instruction Overriding (Role-Playing): Forcing the model to adopt a “role” or persona that overrides its pre-programmed behavioral and ethical guidelines.
  • Chained/Recursive Attacks: Sneaking into the system through a sequence of mini-steps or prompts that, individually, are not dangerous, but collectively break the security policy.
  • Resource/Command Injection (Tool Exploitation): Exploiting external tools (APIs, databases, code) that the LLM has access to in order to execute costly or unauthorized actions.
  • Prompt Leaking (Chain-of-Trust): Eliciting confidential information such as the system prompt (the model’s internal instructions and policies) or details of its architecture.
  • Adversarial Examples: Introducing micro-changes (strange punctuation, invisible characters, or oddities) that confuse the LLM’s parser and divert its response.
  • Trojans/Backdoors: Inserting hidden keywords or phrases that, when invoked, trigger a predefined anomalous or malicious behavior in the model.
  • Social Engineering: Using prompts with highly persuasive content to induce the model to generate content that facilitates fraud, phishing, or disinformation.
  • Data Exfiltration: Using the model to elicit and obtain sensitive or confidential information that is within its current working context.
  • Membership Inference: Finding out if a specific piece of data (like an account number or name) was present in the model’s training set.
  • Covert Channels/Steganography: Hiding malicious instructions in text patterns or encodings that are invisible to a human or automated security validator.

How to Protect Your Company’s AI?

Defense against prompt attacks requires a layered approach that goes beyond a simple spam filter.

✅ Robust Prompt Design (The First Line of Defense): The first step is the most important. It must be unequivocally clear what the model must obey and what it must not, and strict hierarchies must be established for external inputs.

Action: Invest in Prompt and Context Engineering as an essential skill for your team.

✅ Strict Tool Policies: If your LLM interacts with external APIs or databases, set boundaries.

Action: Implement allow-lists for commands and URLs, set cost limits to prevent abuse, and require human reviews for critical operations.

✅ Context Isolation: Confidentiality is key.

Action: Avoid mixing sensitive or proprietary data with public interaction data in the same operational context. Use different instances or models if necessary.

✅ Guardrails and Input/Output Validators: Don’t rely solely on the AI.

Action: Implement security validation systems (validators) that check the user’s input before the model processes it and the output before an action is executed.

✅ Continuous Monitoring and Red-Teaming: Yesterday’s defenses won’t work for tomorrow’s attacks.

Action: Continuously monitor model interactions and conduct red-teaming exercises (simulated attacks) on your own system to proactively find and fix vulnerabilities.