An engineer on your team pastes proprietary source code into ChatGPT to debug a function. That same afternoon, an attacker crafts a sentence, not a script, not an exploit kit, a sentence, that causes your customer-facing AI assistant to dump its system prompt and every guardrail you spent months building.
Both of those happened.
The engineer was at Samsung. The attacker was a Stanford student who typed “Ignore previous instructions” into Bing Chat and extracted Microsoft’s entire confidential prompt architecture.
Neither event required a zero-day. Neither triggered your SIEM.
This guide covers the seven major LLM exploit categories, documents real-world incidents with named companies and measurable consequences, maps defenses to the compliance frameworks you’ll face in your next audit, and provides a prioritized checklist built for organization’s managing AI adoption.
Why Traditional Security Tools Can't Stop LLM Exploits
94% of state-of-the-art LLMs are vulnerable to exploitation. That number comes from a University of Calabria study published in late 2025 that tested 17 production models against direct prompt injection, RAG backdoor attacks, and inter-agent trust exploitation. Sixteen of the seventeen failed.
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Your firewall doesn’t parse natural language inside an HTTPS tunnel. Your WAF doesn’t understand semantic intent. The tools you built your security program around were designed for a different threat model.
According to the OWASP 2025 Top 10 for LLM Applications, prompt injection is the number one vulnerability, appearing in over 73% of production AI deployments during security audits.
HackerOne’s 2025 Hacker-Powered Security Report documented a 540% surge in valid prompt injection reports year-over-year. These aren’t theoretical risks.
They represent the most pressing AI security risks facing production systems right now.
What Are LLM Exploits?
A patched CVE stays patched. An LLM exploit can be rewritten in plain English and resubmitted in seconds.
LLM exploits target the behavior of large language models through natural language input. Because LLMs process instructions and user data through the same channel, the boundary between trusted commands and untrusted content collapses.
The output is probabilistic, not deterministic, meaning the same attack might succeed on one attempt and fail on the next. There is no signature to match. There is no patch to deploy.
The OWASP Top 10 for LLM Applications (2025 edition) maps the most critical risks. The major exploit categories are:
Prompt injection (direct and indirect): Crafted natural language input overrides system-level instructions, causing the model to ignore its guardrails.
Jailbreaking: Adversarial suffixes or social engineering prompts bypass the model’s safety alignment entirely.
Sensitive data exposure: Employees paste confidential data into AI tools, or attackers extract training data containing PII and proprietary information.
Insecure output handling: Unvalidated LLM responses pass executable content (scripts, SQL, markup) into downstream systems
Training data poisoning: Manipulated datasets corrupt model behavior at the supply chain level, embedding backdoors that surface only in production.
Model denial of service: Resource exhaustion prompts force maximum token generation, translating directly into runaway API costs.
Excessive agency: LLM-connected tools and plugins grant operational permissions beyond what the task requires, turning a text leak into remote code execution.
Shubham Khichi, founder and CEO of CyberAGI, and a red team specialist with over a decade of offensive security experience, puts the LLM vulnerabilities bluntly:
The biggest vulnerability I’ve seen is actually it’s pretty human to say that it’s English. LLMs are trained on plain English, and the problem comes in if someone who articulates their words properly in a different tone and different manner, they can basically ask machines to do things for them.