Did you know that in 2023, a single malicious email could trick an AI-powered personal assistant into spamming thousands of users from a victim’s inbox? This isn’t science fiction, it’s a real-world exploit from the OWASP Top 10 for LLM Applications, proving Large Language Models can be played like a fiddle if you skimp on security. Since 2022, LLMs powering chatbots, code generators, and virtual sidekicks are reshaping our daily grind. But here’s the kicker. Their meteoric rise made us almost completely forgetting about security.
This guide hands you the keys to outsmart the biggest LLM security traps, straight from the OWASP Top 10, a battle-tested playbook forged by 500 AI experts.
Understanding the OWASP Top 10 for LLM Applications
The OWASP Top 10 for LLM Applications, updated in 2025, guides you through AI-specific security risks. Over 500 experts from AI firms, cloud providers, and universities identified 43 vulnerabilities, then narrowed them to the 10 most severe through voting and public review. This guide helps developers coding apps, data scientists training models, and security teams protecting systems. It addresses LLM-unique threats like prompt injection and unbounded consumption, overlooked by traditional security. With 70+ percent of organizations using LLMs in 2025, this list is your defense against attackers.
- Prompt Injection: Stop Hackers from Hijacking Your AI
Prompt injection is the LLM equivalent of SQL injection. A manipulated input that alters the system’s behavior. Attackers can craft inputs that hijack the context of an AI conversation and trigger actions the developer never intended. Here’s an example: A resume contains hidden instructions that make the AI declare the candidate as “excellent.”
When an HR bot summarizes it, the manipulation passes through unnoticed. Bad news is you can’t completely prevent it. It exploits the very nature of LLMs and how they work. You can however enforce strict privilege boundaries, introduce user confirmations for sensitive actions, and isolate external content from internal prompts. Basically, treat your LLM like an untrusted user.
- Insecure Output Handling: Don’t Let AI Outputs Run Wild
LLMs don’t just respond in text, they can also write SQL, HTML, JavaScript. If that output flows unfiltered into a backend system or browser, you’ve got a serious vulnerability. For example, a user prompt leads an LLM to generate JavaScript that, when rendered, exfiltrates session tokens from the browser. To mitigate it, Sanitize all output. Use encoding techniques.
Never allow LLM output to directly invoke privileged system calls or script execution. Apply zero-trust principles.
- Training Data Poisoning: Clean Your Data
Because LLMs learn from vast datasets, poisoning can when attackers insert malicious, biased, or false data into these datasets during pre-training or fine-tuning. For example, a competitor floods public forums with biased content targeting a niche domain. That poisoned data gets scraped and used in model training, subtly altering the model’s behavior in the competitor’s favor. To combat this, vet your data sources, enforce sandboxing during data collection, and apply anomaly detection. Maintain a “Machine Learning Bill of Materials” (ML-BOM) to audit data provenance.
- Model Denial of Service: Prevent Overloads
Hackers love overwhelming your LLM with complex inputs, spiking cloud bills or crashing systems. It’s a denial-of-service attack that hits your budget or uptime hard.
Imagine recursive prompts grinding a chatbot to a halt, racking up $10,000 in cloud fees overnight. In 2025, 60% of LLM attacks exploit unchecked resource use. The solution is to set strict input size limits, throttle API requests, and monitor for anomalous usage. Cap resource usage per session or user to avoid runaway costs or exhaustion.
- Supply Chain Vulnerabilities: Secure Your Sources
In traditional software, the supply chain refers to libraries and dependencies. In LLMs, it includes models, datasets, plugins, and infrastructure components, many of which come from third parties. An example would be if a plugin from an unverified source gets installed to extend chatbot functionality. Hidden in its dependencies is code that exfiltrates user queries to a remote server. The solution? Apply rigorous review to models, datasets, and plugins. Use signed components, vulnerability scanning, and SBOM tools. Audit vendor privacy policies, especially if they retrain on your users’ data.
- Sensitive Information Disclosure: Protect Secrets
Your LLM might blab PII or trade secrets without a second thought which means one loose output can break privacy laws or spill your company’s playbook.
In 2023, a prompt tricked an LLM into coughing up health records from training data, landing a firm in hot water. Don’t let your AI be a gossip. For example, user queries a model and receives PII (personally identifiable information) from another user’s past interaction, accidentally retained during training.
The solution would be to sanitize training data, restrict what gets logged, and don’t feed sensitive information into models. Implement clear user policies and opt-out clauses. Fine-tune models with minimal privilege scope.
- Insecure Plugin Design: Tighten Plugin Security
Sloppy plugins are like open windows for hackers, letting malicious inputs waltz in. They can swipe data or jack up privileges in a snap. A 2024 case saw a plugin eat unverified URLs, letting attackers inject code like it’s happy hour. A good example would be a weather plugin accepting a free-form URL. A malicious user redirects it to their own domain, planting instructions that manipulate the system or exfiltrate data.
The solution is to test plugins with SAST/DAST tools to root out flaws. Slap on OAuth2 authentication to keep things legit. Restrict plugin permissions to the bare minimum. Don’t let your AI’s sidekick turn traitor. Lock down those plugins before they burn you.
- Excessive Agency: Control AI Actions
Your LLM or plugins with too much freedom can act like rogue agents, making moves without oversight. Unchecked, they’ll delete data or approve sketchy deals. Here’s an example. A customer service bot has access to delete accounts. A hallucinated or malicious prompt causes it to wipe legitimate user records. Mitigation? Limit plugin capabilities. Use minimal permission scopes. Implement human-in-the-loop approval for destructive or financial operations. Build narrow, purpose-built tools—not generic ones that “do everything.”
Apply least-privilege access to keep your AI on a leash. Demand manual approvals for big moves. Log plugin activity to spot trouble fast. Don’t let your LLM play cowboy, clip its wings before it trashes your systems.
- Overreliance: Verify AI Outputs
Blindly trusting LLM outputs? That’s a true recipe for disaster. Errors, hallucinations, or flaws can slip through, wrecking your plans. When users or systems treat output as gospel, mistakes multiply into real-world damage. Now, this is fine in some cases. Take assistants or AI companion services. Kindroid, Candy AI, Luvr, all of them run LLM in the background. Responses from these models can be interpreted as authoritative, which in that particular situation is not an issue. In other situations however, it is.
Imagine a developer blindly copying LLM-suggested code snippets into production. One of them includes a subtle SQL injection vulnerability. Always review outputs with a human eye, AI’s not infallible. Fine-tune models to fit your needs for better accuracy. Warn users about potential AI flubs to keep them sharp. Don’t let your LLM’s smooth talk fool you, properly check its work, or you’ll pay the price.
- Model Theft: Safeguard Your AI’s Brain
Hackers can clone your proprietary LLM with clever queries, stealing your intellectual edge. It’s like copying your secret sauce, then using it against you. In 2025, a startup lost its edge when a hacker’s API queries rebuilt their model. A good example would be an attacker exploiting a misconfigured storage bucket, downloading a proprietary model, and reselling it under a different name with minimal modifications. Limit API access with tight quotas to block snoops. Watermark outputs to track theft attempts. Watch query patterns like a hawk for sneaky behavior. Don’t let hackers rip off your AI’s smarts, guard its brain like it’s gold.
Practical Steps for Securing LLMs
You’ve seen the traps, now take action. Treat your LLM like a shady stranger, check every input and output against OWASP ASVS rules. Vet training data with ML-BOMs, since 74% of LLM attacks prey on sloppy data controls. Watch APIs and plugins like a hawk for sketchy behavior. Run red-teaming tests to expose cracks before hackers do. Watermark outputs to scare off thieves. As a user, eyeball AI outputs and push for clear labels on generated content. These moves keep your systems from turning into a hacker’s playground. No slacking. Your AI’s counting on you to stay sharp.
Your LLMs are workhorses, but without armor, they’re sitting ducks. The practical guide hands you the tools to squash threats like prompt injection or model theft. With 76% of organizations using LLMs at risk, you can’t snooze. Developers, start checking inputs now. Businesses, grill your plugins and data sources. Users, don’t swallow AI outputs whole, question them. Pick one fix (rate-limiting APIs or testing plugins) and get it done this week.
Hackers keep pace with LLMs, so you’d better outrun them. Remember that 2023 email that turned an AI into a spam cannon? Don’t let yours be the next flop. Lock it down today, and keep your AI from starring in a hacker’s comedy show.
