The Complete Platform for GenAI Security

Focus your resources on innovating with Generative AI, not on securing it.

Generative AI introduces a new array of security risks

We would know. As core members of the OWASP research team, we have unique insights into how Generative AI is changing the cybersecurity landscape.

Brand Reputation Damage

The non-deterministic nature of LLMs poses significant risks to your brand reputation when exposing users to your GenAI apps.

AppSec / OWASP (LLM09)

Data Privacy Risks

The risk of sensitive information disclosure has become increasingly significant in the era of Generative AI: whether it's employees exfiltrating company data to GenAI tools or LLM-based applications revealing sensitive data.

IT / AppSec / OWASP (LLM06)

Denial of Wallet / Service

Denial of Wallet attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with an LLM-based apps leading to substantial resource consumption.

AppSec / OWASP (llm04)

Indirect Prompt Injection

Indirect Prompt Injection occurs when a LLM processes input from external sources that are under the control of an attacker.

AppSec / IT / OWASP (llm01)

Insecure Plugin Design

A potential attacker can construct a malicious request to an LLM plugin, which could result in a wide range of undesired behaviors, up to and including remote code execution.‍

AppSec / IT / OWASP (llm02, llm07)

Jailbreak

Jailbreaking represents a category of prompt injection where an attacker overrides the original instructions of the LLM, deviating it from its intended behavior and established guidelines.

AppSec / OWASP (LLM01)

Legal Challenges

The emergence of GenAI technologies and the accompanying regulatory frameworks is raising substantial legal concerns within organizations.

AppSec / IT

Privilege Escalation

As organizations integrate LLMs with more and more tools within the organization, like databases, APIs, and code interpreters, the risk of privilege escalation increases.

AppSec / OWASP (LLM08)

Prompt Injection

Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs.

AppSec / OWASP (llm01)

Prompt Leak

Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic.

AppSec / OWASP (LLM01, LLM06)

Shadow AI

Employees use dozens of different GenAI tools in their daily operations, most of them unbeknownst to their IT teams. Key concerns are limited visibility, absence of governance, compliance risk, and data exposure.

IT

Toxic, Biased or Harmful Content

A jailbroken LLM behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers if it outputs toxic, biased or harmful content.

AppSec /IT / OWASP (llm09)

Privilege Escalation

As organizations integrate LLMs with more and more tools within the organization, like databases, APIs, and code interpreters, the risk of privilege escalation increases.

AppSec / OWASP (LLM08)

Insecure Plugin Design

A potential attacker can construct a malicious request to an LLM plugin, which could result in a wide range of undesired behaviors, up to and including remote code execution.‍

AppSec / IT / OWASP (llm02, llm07)

Brand Reputation Damage

The non-deterministic nature of LLMs poses significant risks to your brand reputation when exposing users to your GenAI apps.

AppSec / OWASP (LLM09)

Shadow AI

Employees use dozens of different GenAI tools in their daily operations, most of them unbeknownst to their IT teams. Key concerns are limited visibility, absence of governance, compliance risk, and data exposure.

IT

Data Privacy Risks

The risk of sensitive information disclosure has become increasingly significant in the era of Generative AI: whether it's employees exfiltrating company data to GenAI tools or LLM-based applications revealing sensitive data.

IT / AppSec / OWASP (LLM06)

Prompt Injection

Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs.

AppSec / OWASP (llm01)

Denial of Wallet / Service

Denial of Wallet attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with an LLM-based apps leading to substantial resource consumption.

AppSec / OWASP (llm04)

Indirect Prompt Injection

Indirect Prompt Injection occurs when a LLM processes input from external sources that are under the control of an attacker.

AppSec / IT / OWASP (llm01)

Jailbreak

Jailbreaking represents a category of prompt injection where an attacker overrides the original instructions of the LLM, deviating it from its intended behavior and established guidelines.

AppSec / OWASP (LLM01)

Legal Challenges

The emergence of GenAI technologies and the accompanying regulatory frameworks is raising substantial legal concerns within organizations.

AppSec / IT

Prompt Leak

Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic.

AppSec / OWASP (LLM01, LLM06)

Toxic, Biased or Harmful Content

A jailbroken LLM behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers if it outputs toxic, biased or harmful content.

AppSec /IT / OWASP (llm09)

Privilege Escalation

As organizations integrate LLMs with more and more tools within the organization, like databases, APIs, and code interpreters, the risk of privilege escalation increases.

AppSec / OWASP (LLM08)

Insecure Plugin Design

A potential attacker can construct a malicious request to an LLM plugin, which could result in a wide range of undesired behaviors, up to and including remote code execution.‍

AppSec / IT / OWASP (llm02, llm07)

Brand Reputation Damage

The non-deterministic nature of LLMs poses significant risks to your brand reputation when exposing users to your GenAI apps.

AppSec / OWASP (LLM09)

Shadow AI

Employees use dozens of different GenAI tools in their daily operations, most of them unbeknownst to their IT teams. Key concerns are limited visibility, absence of governance, compliance risk, and data exposure.

IT

Data Privacy Risks

The risk of sensitive information disclosure has become increasingly significant in the era of Generative AI: whether it's employees exfiltrating company data to GenAI tools or LLM-based applications revealing sensitive data.

IT / AppSec / OWASP (LLM06)

Prompt Injection

Prompt Injection is a cybersecurity threat where attackers manipulate a large language model (LLM) through carefully crafted inputs.

AppSec / OWASP (llm01)

Denial of Wallet / Service

Denial of Wallet attacks, alongside Denial of Service, are critical security concerns where an attacker excessively engages with an LLM-based apps leading to substantial resource consumption.

AppSec / OWASP (llm04)

Indirect Prompt Injection

Indirect Prompt Injection occurs when a LLM processes input from external sources that are under the control of an attacker.

AppSec / IT / OWASP (llm01)

Jailbreak

Jailbreaking represents a category of prompt injection where an attacker overrides the original instructions of the LLM, deviating it from its intended behavior and established guidelines.

AppSec / OWASP (LLM01)

Legal Challenges

The emergence of GenAI technologies and the accompanying regulatory frameworks is raising substantial legal concerns within organizations.

AppSec / IT

Prompt Leak

Prompt Leak is a specific form of prompt injection where a Large Language Model (LLM) inadvertently reveals its system instructions or internal logic.

AppSec / OWASP (LLM01, LLM06)

Toxic, Biased or Harmful Content

A jailbroken LLM behaving unpredictably can pose significant risks, potentially endangering an organization, its employees, or customers if it outputs toxic, biased or harmful content.

AppSec /IT / OWASP (llm09)

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Enterprise-Grade GenAI Security

Fully LLM-Agnostic

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GenAI Red Teaming

Uncover GenAI risks and vulnerabilities in your LLM-based applications

Identify vulnerabilities in your homegrown applications powered by GenAI with Prompt Security’s Red Teaming.

Prompt Security Dashboard

Trusted by Industry Leaders

“In today's landscape, every CISO must navigate the tricky balance between embracing GenAI technology and maintaining security and compliance. Prompt serves as the solution for those who aim to facilitate business growth without compromising data privacy and security.”

Mandy Andress

CISO, Elastic

“Prompt Security has been an invaluable partner in ensuring the security and integrity of our multi-agent Generative AI application, ZOE. I anticipate that the criticality of protecting our AI from prompt injections and other adversarial attacks will rise significantly over the next year, as those techniques become more wide-spread and publicly available. Prompt Security’s industry-leading expertise in detecting and preventing prompt injections, as well as other flavors of Large Language Model attacks, has given us peace of mind, ensuring that our AI application can consistently deliver trustworthy results, fully protected from malicious abuse. Their dedication to cybersecurity and the innovative field of LLM security measures is truly commendable.”

Dr. Danny Portman

Head of Generative AI, Zeta Global

"Prompt is the single user-friendly platform that empowers your organization to embrace GenAI with confidence. With just a few minutes of onboarding, you gain instant visibility into all GenAI within your organization, all while ensuring protection against sensitive data exposure, prompt injections, offensive content, and other potential concerns. It's truly an exceptional product!"

Guy Fighel

Senior VP, New Relic

"I had the pleasure working and collaborating with Itamar as core members of the OWASP Top 10 for Large Language Model Applications, where we mapped and researched the threat landscape of LLMs, whether your users are just using existing application or developing ones themselves. I found Prompt Security’s approach to reduce the attack surface of LLM applications as powerful, realtime, providing true visibility of the detected threats, while offering practical ways to mitigate it, all with minimal impact to teams’ productivity."

Dan Klein

Director, Cyber Security Innovation R&D Lead at Accenture Labs & OWASP Core team member for top 10 llm apps

“In today's business landscape, any organization that embraces GenAI technology (and they all should) understands that it introduces a fresh array of risks, ranging from Prompt Injection and potential jailbreaks to the challenges of managing toxic content and safeguarding sensitive data from being leaked. Rather than attempting to address these risks on your own, which can waste a significant amount of time, a more effective approach is to simply onboard Prompt. It provides the peace of mind we've been seeking.”

Assaf Elovic

Head of R&D, Wix

“If you're looking for a simple and straight-forward platform to help in your organization's safe and secure adoption of GenAI, you have to check out Prompt.”

Al Ghous

CISO, Snapdocs

“I like Prompt Security. It adds an important layer of GPT safety while maintaining user privacy. I'm not sure what I'd do without Prompt.”

Jonathan Jaffe

CISO, Lemonade Insurance

Time to see for yourself

Learn why companies rely on Prompt Security to protect both their own GenAI applications as well as their employees' Shadow AI usage.

Prompt Security Dashboard