Skip to main content
Service

AI Chatbot Security 

What happens when a chatbot spills secrets or follows a malicious prompt? One slip can expose data, break trust, and trigger compliance issues. Strobes AI Chatbot Security Testing runs controlled attack scenarios to reveal how your bots respond under pressure and where security gaps exist.

Request Pentest

Our Approach to AI Chatbot Security Testing

Chatbots are the new front door to your business, and attackers know it. Strobes AI Chatbot Security Testing puts every angle of conversational AI under pressure to expose risks before they cause damage.

svg+xml;charset=utf
1

Prompt Injection

Hidden instructions, multi-turn traps, and cross-language exploits are simulated to check if a chatbot can be manipulated into revealing sensitive data or misbehaving.
2

Session Security

Tokens, logins, and active chats are tested against leaks, tampering, and bypass attempts to uncover weaknesses in how sessions are created and maintained.
3

Output Safety

Responses are evaluated for malicious code, script injection, and unsafe content, ensuring chatbot outputs cannot be turned into attack vectors.
4

Memory & Context

Conversation history, context windows, and long-term memory are stressed for poisoning, manipulation, and corruption to expose deeper security risks.

Why Fortune 500 Companies Choose Strobes

Firewalls Don’t Speak AI

Firewalls block traffic, not logic. Malicious prompts, hidden instructions, and multi-turn traps bypass static rules and directly exploit chatbot reasoning.

WAFs Miss Chatbot Exploits

Web application firewalls were never designed for AI. They fail to detect prompt injections, cross-language bypasses, and chatbot-specific logic flaws.

Pentests Ignore Model Logic

Standard pentests target infrastructure, not intelligence. They miss poisoned training data, adversarial inputs, and memory corruption that destabilize AI models.

AI Needs AI-Native Testing

Generic tools can’t validate AI under stress. Only AI-specific testing reveals resilience against data leaks, prompt manipulation, and unsafe outputs.

Monitoring Stops at the Surface

SIEMs track network anomalies, not conversations. They overlook subtle manipulations inside chats where attackers bend AI behavior undetected.

Compliance Isn’t Coverage

Compliance Isn’t Coverage
Audit reports prove paperwork, not security. Regulations lag while untested AI chat flows expose sensitive data and mislead users in production.

Why Choose Strobes for AI Chatbot Security Testing

Prompt injection, cross-session leakage, malicious outputs, and memory poisoning tested using attacker techniques.

Chatbots stressed with poisoned histories, crafted prompts, and hijack attempts to measure resilience.

Every reported vulnerability rechecked after remediation, ensuring risks are resolved and resilience confirmed.

Reports translate chatbot flaws into business, regulatory, and trust impact for clear executive alignment.

Real Incidents Involving AI Chatbots

Attack

A Redis client bug created cross-session data leakage.

Result

Some users saw other customers’ chat titles, and for 1.2% of ChatGPT Plus subscribers (around 120,000 accounts), billing information such as name, email, payment address, and last four digits of credit cards was exposed.

Impact

The breach lasted 9 hours before OpenAI patched it.

Attack

Queries exposed financial and operational data across unrelated accounts.

Result

Users were able to access business records and transaction details from other organizations. The number of affected users has not been disclosed, but multiple accounts were impacted simultaneously.

Impact

Sage disabled the Copilot feature immediately and confirmed an internal investigation was underway to prevent recurrence.

Attack

An XSS flaw allowed malicious prompts to run inside chatbot conversations.

Result

The vulnerability exposed session cookies that could let attackers hijack support agent accounts and impersonate them during live sessions.

Impact

Independent researchers estimated tens of thousands of sessions were at theoretical risk; Lenovo issued a security patch shortly after public disclosure.

Attack

Poor access controls allowed attackers to alter numeric identifiers in API traffic.

Result

Unauthorized users could retrieve chat prompts and answers from other people’s sessions, exposing sensitive private queries.

Impact

Meta confirmed the flaw, fixed it quickly, and paid the security researcher under its bug bounty program; the payout amount was undisclosed.

Our Research & Blog

Test Your Chatbots Against Real Attacks Today

Close Menu