Secure your AI applications against emerging threats with CipherTrivia’s AI security services. We assess AI models and LLM-based applications to identify vulnerabilities involving prompt injection and sensitive data exposure while evaluating access controls and AI agent security. Our security testing helps organizations understand AI-related risks and prioritize remediation before vulnerabilities can be exploited.
AI security services help organizations identify and address vulnerabilities in AI-powered applications and large language models (LLMs) along with AI agents and connected systems. These services include security assessments and vulnerability testing to reduce risks involving data exposure and unauthorized access.
As businesses adopt generative AI and autonomous AI agents, securing models alone is no longer sufficient. AI agents interact with APIs and enterprise data while executing tasks and making decisions based on user instructions. Weak access controls and excessive permissions can expose sensitive information or allow agents to perform unauthorized actions. CipherTrivia provides AI security services to identify vulnerabilities across AI applications and LLM-based systems as well as agentic AI workflows.
Our AI security assessments evaluate risks such as prompt injection and sensitive data exposure along with insecure tool integrations and excessive AI agent permissions. We assess how AI agents access resources and process instructions while interacting with connected systems to identify potential attack paths and security gaps. Our findings and remediation recommendations help organizations strengthen AI application security and adopt AI agents with greater control over data access and automated actions.
AI-specific risks
User Prompts
AI Application
LLM
AI Agent
APIs and External Tools
Enterprise Documents
RAG Retrieval
Connected Systems
AI agent cybersecurity testing evaluates autonomous AI agents for security weaknesses that could allow attackers to manipulate agent behavior or trigger unauthorized actions. Testing focuses on risks such as indirect prompt injection and excessive permissions along with insecure tool integrations and unauthorized data access.
AI agents introduce new cybersecurity risks because they can interpret instructions and interact with APIs or external tools while accessing sensitive business data. Security weaknesses such as prompt injection and excessive permissions can manipulate agent behavior or trigger unauthorized actions. CipherTrivia provides AI agent cybersecurity testing to identify vulnerabilities in agent workflows and evaluate the effectiveness of security controls.
Our AI agent security testing assesses agent permissions and tool integrations along with authentication mechanisms and data access boundaries. We simulate relevant attack scenarios to identify risks such as unauthorized tool execution and indirect prompt injection as well as sensitive data exposure and privilege escalation. Our testing findings include risk-based recommendations to help organizations strengthen AI agent security and reduce the likelihood of exploitation across connected systems.
CipherTrivia provides AI security services to identify vulnerabilities across AI-powered applications and intelligent systems. Our testing approach covers LLM-based applications and generative AI solutions along with AI agents and connected data sources to help organizations address security risks before they can be exploited.
We assess AI-powered applications for security weaknesses in authentication and authorization along with input validation and application logic. Our testing identifies vulnerabilities that could expose sensitive information or allow unauthorized access through AI-driven features.
Our LLM security testing evaluates large language model applications for prompt injection and sensitive data leakage along with insecure output handling and weaknesses in model access controls. We identify risks that could affect application security and the confidentiality of business information.
AI agent cybersecurity testing evaluates the security of autonomous agents that interact with APIs and external tools or execute tasks using connected systems. We test for indirect prompt injection and excessive permissions along with unauthorized tool execution and privilege escalation to identify weaknesses that could lead to unintended actions or data exposure.
We assess generative AI applications for vulnerabilities involving user prompts and generated responses as well as data processing and third-party integrations. This helps organizations understand security risks associated with deploying AI-powered features in business environments.
Our AI red teaming services simulate controlled adversarial scenarios to evaluate how AI systems respond to malicious inputs and attempts to bypass security restrictions. This testing helps uncover weaknesses in model behavior and application safeguards that may not be identified through conventional security testing alone.
Retrieval-Augmented Generation (RAG) systems connect AI models with enterprise documents and external knowledge sources. We assess retrieval permissions and document access controls along with data handling mechanisms to identify vulnerabilities that could expose confidential information to unauthorized users or AI agents.
AI applications often depend on APIs and external services to retrieve information or execute tasks. We evaluate API authentication and authorization along with input validation and integration security to identify weaknesses that could expose connected systems or allow unauthorized operations.
We assess relevant AI model interfaces and deployment configurations to identify security weaknesses involving model access and inference endpoints along with exposed functionality and inadequate protective controls. Our findings help organizations strengthen the security of their AI model deployments.
CipherTrivia follows a structured AI security testing process to identify vulnerabilities across AI applications and LLM-based systems along with autonomous AI agents. Our approach combines security reviews with controlled testing to uncover potential attack paths and provide actionable recommendations.
We identify the AI models and applications within scope while reviewing system architecture and data flows along with connected APIs and agent integrations. This helps establish the testing objectives and identify critical security boundaries.
We analyze potential attack scenarios involving prompt injection and sensitive data exposure along with insecure tool access and excessive AI agent permissions. This step helps prioritize the components and workflows that require focused security testing.
We conduct authorized security tests against relevant AI components to identify vulnerabilities in prompts and model responses as well as API integrations and agent workflows. Testing may include attempts to bypass security restrictions or trigger unauthorized tool execution.
We validate identified weaknesses and evaluate their potential impact on application security and business data. Findings are prioritized based on severity and exploitability so organizations can focus on the most significant risks.
We deliver a detailed AI security assessment report covering identified vulnerabilities and supporting evidence along with risk ratings and practical remediation recommendations. Retesting can be performed when included in the agreed scope to validate implemented fixes.
CipherTrivia helps organizations identify and address security vulnerabilities across AI-powered applications and autonomous AI agents. Our approach focuses on AI-specific attack scenarios and practical security testing to help businesses understand their exposure and strengthen protection across connected systems.
We focus on vulnerabilities affecting LLMs and generative AI applications along with AI agents and connected APIs. Our testing examines risks that conventional application security assessments may not fully address.
We assess autonomous AI agents for indirect prompt injection and excessive permissions along with unauthorized tool execution and insecure access to connected resources. This helps identify weaknesses that could lead to data exposure or unintended actions.
Our AI security testing identifies exploitable weaknesses through controlled testing and structured analysis. We document findings with relevant evidence and prioritize risks according to their potential security impact.
We provide clear recommendations that help development and security teams address identified vulnerabilities and improve safeguards across AI applications and agent workflows.
We evaluate relevant security controls across AI models and application interfaces along with APIs and connected data sources. This broader assessment helps uncover risks that may arise from interactions between different system components.
Our reports organize findings by severity and explain their potential impact while outlining corrective actions. This gives organizations a clear basis for prioritizing remediation and strengthening their AI security posture.
AI systems introduce security risks that differ from those found in traditional applications. Our AI security assessments focus on identifying weaknesses that could affect data confidentiality and application integrity or allow unauthorized system actions.
Prompt Injection: Malicious instructions can manipulate an AI system into ignoring intended restrictions or disclosing protected information.
Sensitive Data Exposure: Inadequate safeguards can expose confidential information through prompts and model responses or connected data sources.
Insecure Output Handling: Unvalidated AI-generated content can introduce security risks when applications process or execute the output.
Broken Access Controls: Weak authorization mechanisms can allow users or AI agents to access resources beyond their assigned permissions.
RAG Data Exposure: Improper retrieval permissions can expose documents or records that a user should not be able to access.
Excessive AI Agent Permissions: Overprivileged agents may invoke tools or perform actions beyond their intended scope.
Insecure API Integrations: Weak authentication and insufficient validation across connected services can expose AI application components to attacks.
Model Abuse and Manipulation: Adversarial inputs can exploit weaknesses in model behavior and undermine intended security restrictions.
Our AI security assessment provides a clear view of identified vulnerabilities and the actions required to strengthen your AI application's security posture.
AI Security Assessment Report: A structured report covering the components assessed and the vulnerabilities identified during testing.
Vulnerability Details: Technical explanations of security weaknesses with supporting evidence where applicable.
Risk Severity Ratings: Prioritization of findings based on their potential impact and exploitability.
Remediation Recommendations: Practical guidance to address identified weaknesses and improve security controls.
AI Attack Scenario Findings: Results from relevant tests involving prompt injection and data exposure or unauthorized AI agent actions.
Retesting Results: Validation of implemented fixes when retesting is included in the engagement scope.
AI security testing helps organizations identify vulnerabilities that could expose confidential information or compromise connected applications. It also helps assess risks associated with AI-generated outputs and automated agent actions before these systems are deployed or expanded.
AI security testing can identify prompt injection and sensitive data leakage along with insecure output handling and broken access controls. AI agent assessments can also uncover excessive permissions and unauthorized tool execution or weaknesses in API integrations.
Traditional application security testing focuses on vulnerabilities such as injection flaws and broken authentication within conventional software. AI security testing also examines AI-specific risks involving prompts and model behavior along with retrieval mechanisms and agent interactions with external tools.
Prompt injection occurs when malicious instructions influence an AI system to disregard intended restrictions or process untrusted instructions in an unsafe way. It can lead to sensitive data exposure or unintended actions when the application lacks appropriate security controls.
Yes. AI agent security testing can evaluate agent permissions and tool integrations along with access boundaries and safeguards against unauthorized actions. It can also assess how indirect prompt injection may affect agents that retrieve external content or interact with connected systems.
AI red teaming uses controlled attack simulations to test the resilience of AI applications against malicious inputs and attempts to bypass security restrictions. It helps identify weaknesses in model behavior and application safeguards while providing evidence for remediation.
Yes. LLM and RAG security assessments can examine prompt handling and model responses along with retrieval permissions and access to connected documents. Testing helps identify weaknesses that could expose confidential information or allow unauthorized access to enterprise data.
An AI security assessment report typically includes identified vulnerabilities and their severity ratings along with supporting evidence and remediation recommendations. Depending on the agreed scope, it may also include attack scenarios and retesting results.
Businesses should consider AI security testing before deploying AI applications into production and after significant changes to models or integrations. Periodic assessments are also valuable when new AI agents or external tools are introduced.
CipherTrivia helps organizations identify AI-specific vulnerabilities through structured security assessments and controlled testing. Its assessment approach covers relevant AI applications and agent workflows while providing risk-based findings and practical remediation guidance.
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