> AI Agent Security Audit_
An AI agent does more than answer questions. It reads email, tickets and web pages, calls tools, runs code and acts on your systems with real permissions. Any text it reads can try to change what it does next.
An AI agent security audit from Krasty Soft tests what an attacker could make your agent do. We review the code and configuration around the model, trace every source of untrusted text to the tools it can reach, and give you a ranked list of fixes.
> When Should You Conduct an AI Agent Security Audit?_
The risk of an agent grows with its access. We recommend an audit at these points:
- Before you give an agent access to customer data, payments, email or production systems.
- Before launching an agent that reads content from users, the web or public repositories.
- When you connect new tools, MCP servers or third-party integrations.
- When moving from a single assistant to a multi-agent workflow.
- Before an enterprise sale or a compliance review that asks how AI risks are controlled.
- After an incident, such as an agent taking an action nobody approved.
> What Is Included in Our AI Agent Security Audit_
Each AI agent security assessment looks at the whole system, not only the prompt. The model's behaviour changes between versions, but the code that decides what it can reach does not. That is where we focus, across five areas.
AI Agent Architecture and Configuration Review
We map the agent's components: models, system prompts, tools, memory, retrieval sources and the services it runs on. Then we check the configuration: which actions are enabled, which are granted by wildcard, where keys are stored and which safeguards are switched off by default.
Prompt Injection and Instruction Manipulation Testing
We find every place where text from outside reaches the model's context: chat messages, documents, web pages, emails, issue comments and tool results. For each one we check who can write that text and which tools the agent holds while reading it.
We then test direct and indirect prompt injection against those paths. The goal is not a clever jailbreak, but proof of whether planted instructions can trigger a real action.
Tool Access and Permission Security Assessment
We review every tool and integration the agent can call, and compare its permissions with what the task really needs. Agents that read untrusted content should hold the smallest possible set of tools.
We also check how high-risk actions are approved. An approval that arrives after the email is sent or the file is deleted protects nothing.
Data Access, Memory and Context Security
We test whether the agent can reveal private data, system prompts or other users' conversations. For RAG systems, we review how documents are ingested, how embeddings are stored and whether retrieval respects each user's permissions.
We also check memory poisoning: whether an attacker can plant false facts or instructions that the agent will trust in later sessions.
Code Execution and Multi-Agent Communication Security
If your agent runs code, we review the sandbox: file system access, network rules, secrets in the environment and escape paths to the host. In multi-agent systems, we check what each agent trusts from the others and whether a compromised agent can pass instructions up the chain.
> Common AI Agent Security Risks and Vulnerabilities_
An AI agent vulnerability assessment usually uncovers issues from this list, which follows the OWASP Top 10 for LLM Applications:
- Indirect prompt injection through documents, web pages, emails or comments.
- Excessive agency: tools and permissions far beyond what the task needs.
- Data leakage of system prompts, credentials and customer data.
- Identity confusion: trust granted by a display name or another field the sender controls.
- Poisoned memory and RAG sources that persist across sessions.
- Unsafe code execution and weak sandboxing.
- Missing limits on cost, loops and the number of actions per task.
These are not theoretical. In our review of Open Session, a comment from any GitHub account reached an agent with admin tools. In another project, a display name was enough to get owner-level access to the agent. Both teams fixed the issues after our reports.
> Our AI Agent Security Audit Process_
Our process is designed to give you findings you can fix, not a list of model quirks.
- Scoping
We agree on the agents, tools, data sources and environments in scope.
- Mapping
We trace every input that reaches a prompt and every tool and credential the agent can use.
- Review and testing
We review the code statically and, where agreed, test injection paths in a safe environment.
- Reporting
Each finding is tied to files and lines, rated by severity and paired with a remediation step.
- Debrief and retest
We walk your team through the results and verify the fixes.
> Benefits of a Professional AI Agent Security Audit_
A professional audit gives you control over a system that is hard to predict:
Your agent cannot be turned against your users or your infrastructure.
Customer data and user privacy are protected in prompts, logs and memory.
High-risk actions require approval at the right moment.
You have evidence for compliance reviews and enterprise security questionnaires.
Your team gets design patterns that keep new tools and agents safe.
> Industries & Use Cases_
We review coding agents, customer support agents, inbox and calendar assistants, internal knowledge bots built on RAG, and agent platforms that host other people's agents. The audit fits AI startups and SaaS companies adding agent features to an existing product. It is especially important in healthcare, fintech and legal services, where an agent that exposes data creates legal and financial risk.
We also check reliability, not only security. In our AI agent gateway review, large prompts failed silently with no error for support to see.
> FAQ About AI Agent Security Audit and Assessment_
These are the questions teams ask us most often before an agent audit.
Can You Assess the Security of Multi-Agent Systems?
Yes. We map how agents pass tasks and messages to each other, which agent holds which tools, and where untrusted text can travel through the chain. The weakest agent often decides the security of the whole system.
Are Third-Party Tools and API Integrations Included in the Assessment?
Yes. We review every tool, MCP server and API the agent can call, including the permissions and credentials it uses. We test how your system uses them, not the providers' own infrastructure.
What Deliverables Can We Expect After the Assessment?
You receive a written report with every finding ranked by severity, tied to specific files and lines, and paired with a recommended fix. We also hold a walkthrough session and can verify the fixes.
How Often Should AI Agents Undergo Security Testing?
Test before launch, whenever you add tools, data sources or agents, and after major model upgrades. Agent capabilities change fast, so a yearly audit alone is rarely enough. Between audits, keep a set of planted-instruction tests in your test suite and run it on every release.