AI cybersecurity: Why attackers are targeting LLMs

As AI adoption accelerates across industries, large language models (LLMs) and generative systems are becoming prime targets for exploitation. From customer service chatbots to internal copilots and decision-making engines, these tools now process sensitive data, steer critical operations, and influence human decisions. That’s exactly what makes them so attractive to attackers.

Unlike traditional applications, LLMs are designed to receive and generate unfiltered input. This introduces a completely new surface area for attack—one that many teams are still learning how to secure. If your organization is building or integrating AI systems, understanding the top AI cybersecurity risks is non-negotiable. Keep reading to learn what makes these systems vulnerable, how attackers are exploiting them, and what you can do to proactively secure your AI stack.

The real-world threats of AI misuse and misalignment

One of the most underappreciated attack surfaces in AI cybersecurity is misuse. When LLMs are deployed without strict controls on how they’re used—or misused—they can be prompted to behave in unexpected or harmful ways.

Examples of misuse include:

• Instruction reversal: Tricking an assistant into ignoring safeguards by rephrasing a prompt creatively.
• Impersonation: Leveraging AI-generated content to mimic trusted sources or internal communications.
• Content laundering: Using LLMs to rephrase or obfuscate malicious intent.
  

Misalignment adds another layer of risk. Even with the best prompt training, an LLM’s actual behavior can diverge from the organization’s security expectations. This is particularly dangerous in contexts involving:

• Autonomous agents
• Decision-making assistants
• Sensitive or regulated industries (finance, healthcare, legal)

When misuse and misalignment combine, attackers can weaponize your AI model to perform harmful actions while remaining under the radar.

Prompt-based attacks: The new injection vulnerability

Prompt injection has become one of the most pressing AI security threats. Similar to SQL injection in traditional web apps, this technique exploits the model’s natural language interface to override original instructions.

Two primary types of prompt injection:

1. Direct prompt injection
   The attacker inserts malicious instructions directly into the prompt field, often by chaining commands or hiding them within harmless-seeming input.
2. Indirect prompt injection
   The attacker embeds malicious instructions into external content that the LLM later consumes—such as a web page, email, or code snippet.

What’s at stake?

• Leakage of sensitive training data
• Unintended actions from autonomous agents
• Manipulation of outputs and decision-making
• Loss of trust in AI-driven systems

If your AI assistant fetches third-party data, scans emails, or operates across multiple domains, prompt injection should be a top concern.

From reactive to proactive: Building a secure AI stack

Reactive security—patching vulnerabilities after they’re exploited—doesn’t work with AI systems. The attack surface is dynamic, and many vulnerabilities emerge from model behavior, not code bugs. To reduce exposure, organizations need to embed security across every stage of AI development and deployment.

Here’s how to shift toward a proactive AI cybersecurity posture:

• Threat modeling for AI
  Identify misuse scenarios, high-risk prompts, and trust boundaries early in development. Think like an attacker to find edge cases and system failures.
• Guardrails and policy enforcement
  Use prompt filtering, output validation, and strict context management. Avoid letting models consume arbitrary external input without verification.
• Red teaming and adversarial testing
  Regularly test your models with simulated attacks. This includes prompt injection, jailbreak attempts, and alignment stress tests.
• Data minimization and access controls
  Limit the data your models have access to. Always separate sensitive business logic from model interaction layers.
• Automated vulnerability detection
  Integrate tools that continuously test and monitor for AI-specific vulnerabilities—similar to how Strike enables continuous pentesting for traditional systems.

What’s next for securing AI systems?

As LLMs become more capable and integrated into business operations, attackers will continue to find new ways to manipulate them. But organizations don’t have to wait for a breach to take action.

At Strike, we’re helping security teams stay ahead of AI cybersecurity threats by integrating continuous testing, ethical hacking, and AI-specific offensive simulations into their development cycles. Whether you're building your own models or relying on third-party AI tools, security should be part of the system design, not an afterthought.