Deploying an AI chatbot without testing its security is like building a bank with a glass front door. It looks modern, it's easy for customers to use, but anyone with a hammer can walk right in.
Since the release of ChatGPT, the security community (led by OWASP's LLM Top 10 project) has identified a specific set of attack vectors that are unique to large language models. These aren't software bugs in the traditional sense; they are exploits of the model's logic and helpfulness.
Here are the 7 vulnerabilities we see most often during Centuri audits, and what you need to do to fix them.
1. Prompt Injection
The "root" of most AI security issues. An attacker provides instructions that override the system's core rules. The Fix: Use external output filters and strict context isolation. Read more →
Mini Example
2. AI Jailbreaking
Using roleplay or adversarial framing to bypass the model's safety filters (like making napalm or using hate speech). The Fix: Adversarial testing with local "red team" models to find weak points. Read more →
3. System Prompt Disclosure
Tricking the bot into outputting its hidden instructions, which often contains sensitive business logic or API data. The Fix: Keyword filtering on the bot's responses to block mentions of system-specific terms. Read more →
4. Authority Framing
Impersonating an IT member or manager to get the bot to grant access. The Fix: Zero-trust architecture where the bot cannot take privileged actions without out-of-band verification. Read more →
5. Social Engineering
Manipulating the bot's "desire to be helpful" through fabricated prior context or emotional urgency. The Fix: Session-limited memory and behavioral constraints. Read more →
6. Cross-Session Data Leaks
When the bot "remembers" data from User A and repeats it to User B. The Fix: Namespace-isolated databases and strict data retrieval ACLs. Read more →
7. AI Persona Override
Roleplaying as a hacker or pirate to ignore safety constraints. The Fix: Persona anchoring in the system prompt and secondary behavioral monitoring. Read more →
Note on OWASP: These 7 items align closely with the OWASP Top 10 for LLM Applications, which is the global standard for AI security. Any enterprise-grade AI system should be audited against this framework.
Why Traditional Pentesters Miss These
Standard security audits focus on servers, firewalls, and code. They look for SQL injection or broken authentication. But AI vulnerabilities are semantic. They exist in the language, not the infrastructure. You can have a perfectly secure server and a perfectly vulnerable AI bot.
That's why a specialized AI audit is required. You need to test the model's behavior under adversarial conditions.
Get the full AI security checklist.
We'll audit your bot against all 7 of these vulnerabilities (and many more) and give you a prioritized remediation plan.