Repello AI - AI Red Teaming

AI Red Teaming

AI Red

Teaming

The only way to secure your AI systems is to know

where it’s broken, we can help!

The only way to secure your AI systems is to know

where it’s broken, we can help!

Secure your AI

About us

Dedicated to securing your AI systems against cyber threats.

Securing AI applications in production is crucial as organizations scale.

We're here to help you navigate the complex landscape of AI security.

At Repello AI, we specialize in

AI Red Teaming services

At Repello AI, we specialize in

AI Red Teaming services

At Repello AI, we specialize in

AI Red Teaming services

Stress-test your AI applications to detect and neutralize threats before deploying to production and avoid any surprises with Repello AI’s threat modelling.

Have world’s best red-teaming experts break your AI application - with white-glove onboarding and context-aware methodology.

About us
About us
About us

Your organisation's AI system

are a new attack vector

Your organisation's AI system are a new attack vector

For the(bad guys/threats actors) to compromise

the overall security

For the(bad guys/threats actors) to compromise

the overall security

For the(bad guys/threats actors) to compromise the overall security

Get started

Your Challenges

Prompt injections
Intelligent inputs can influence a Large Language Model (LLM), causing unexpected reactions. Overt prompt encroachments supplant system directions. This vulnerability might lead to the LLM producing damaging or undesirable outcomes.
Brand reputation damage
Inaccurate or inappropriate responses generated by LLMs can result in severe damage to a company's brand reputation. If an LLM produces offensive, biased, or misleading content, it can quickly spread online and harm the perception of the organization.
Model theft
Unauthorized access, copying, or exfiltration of proprietary LLM models can have serious consequences. This includes economic losses, compromised competitive advantage, and potential access to sensitive information.
Sensitive data leak
LLMs may unintentionally reveal confidential or sensitive information in their responses. This could lead to unauthorized data access, privacy violations, and security breaches.
Toxicity/bias
LLMs trained on biased or tainted datasets can produce outputs that exhibit toxicity or bias. This poses ethical concerns and can result in discriminatory or unfair content generation.
Data exfiltration attacks
Indirect prompt injections can be utilized by malicious actors to extract sensitive or confidential information from an LLM. By carefully crafting inputs, attackers can manipulate the LLM to generate responses that inadvertently disclose valuable data.
Denial of wallet attacks leading to resource exhaustion
Attackers can manipulate LLMs to perform resource-intensive operations, leading to service degradation or high costs. The resource-intensive nature of LLMs, combined with unpredictable inputs, can result in exhaustion of computational resources.

Prompt injections
Intelligent inputs can influence a Large Language Model (LLM), causing unexpected reactions. Overt prompt encroachments supplant system directions. This vulnerability might lead to the LLM producing damaging or undesirable outcomes.
Brand reputation damage
Inaccurate or inappropriate responses generated by LLMs can result in severe damage to a company's brand reputation. If an LLM produces offensive, biased, or misleading content, it can quickly spread online and harm the perception of the organization.
Model theft
Unauthorized access, copying, or exfiltration of proprietary LLM models can have serious consequences. This includes economic losses, compromised competitive advantage, and potential access to sensitive information.
Sensitive data leak
LLMs may unintentionally reveal confidential or sensitive information in their responses. This could lead to unauthorized data access, privacy violations, and security breaches.
Toxicity/bias
LLMs trained on biased or tainted datasets can produce outputs that exhibit toxicity or bias. This poses ethical concerns and can result in discriminatory or unfair content generation.
Data exfiltration attacks
Indirect prompt injections can be utilized by malicious actors to extract sensitive or confidential information from an LLM. By carefully crafting inputs, attackers can manipulate the LLM to generate responses that inadvertently disclose valuable data.
Denial of wallet attacks leading to resource exhaustion
Attackers can manipulate LLMs to perform resource-intensive operations, leading to service degradation or high costs. The resource-intensive nature of LLMs, combined with unpredictable inputs, can result in exhaustion of computational resources.