Independent expert Andrey Biryukov examines how AI agents transition from simple chat interfaces to autonomous systems capable of reading files, sending emails, calling APIs, and managing repositories, thereby becoming targets for a new class of threats.
Unlike conventional chatbots, agents possess autonomy that introduces risks to confidentiality, integrity, and availability. Confidentiality breaches occur when agents read unauthorized files or exfiltrate data through tool outputs, workspace files, memory logs, or webhooks. Integrity violations arise when agents delete files, misroute emails, initiate fraudulent transactions, or select suboptimal vendors. Availability issues emerge when long-running tasks or browser automation create cascading failures that lock entire pipelines.
Attacks via Trusted Content
Prompt injection remains the primary vector because large language models cannot reliably distinguish user instructions from processed data. Malicious commands embedded in web pages, emails, code comments, or Jira tasks can be executed with high trust. Researchers at NeuralTrust demonstrated that specially crafted strings resembling URLs caused OpenAI Atlas to interpret instructions instead of navigating, bypassing normal validation. Zscaler ThreatLabz created a fake Python library documentation site containing hidden instructions in CSS and JSON-LD metadata that convinced coding agents to purchase a $3 license key via the attacker’s cryptocurrency wallet; four of 26 tested models complied.
Data Poisoning and Backdoor Attacks
Data poisoning targets the training or fine-tuning phase. Researchers from Carnegie Mellon and Cornell Tech altered public datasets on immigration, hiring discrimination, racial disparities, autonomous driving, and AI motivation, then uploaded tampered versions to private repositories. Agents from Anthropic, OpenAI, and Google selected the poisoned datasets in approximately half of queries. In medical imaging, insertion of just 250–300 poisoned samples into a one-million-image pneumonia dataset (0.025 %) was sufficient to implant a backdoor that caused the model to miss diagnoses in specific demographic groups.
Tool and Protocol Vulnerabilities
Three vulnerabilities in Git MCP Server allowed agents to escape repository boundaries and combine file-system, terminal, and connector access to execute malicious code. Comparative testing of Function Calling versus Model Context Protocol architectures revealed distinct risk profiles: Function Calling proved more resistant to direct prompt injection but more susceptible to tool manipulation, while MCP offered better component isolation yet enabled more cross-component attacks. Composite attacks succeeded far more often than isolated ones across 3,250 test scenarios.
Defense Recommendations
OWASP published the Agentic Top 10 listing risks such as Agent Goal Hijack, Tool Misuse, identity abuse, supply-chain compromise of MCP components, and memory poisoning. Yandex and Kaspersky Lab advocate applying STRIDE and OWASP methodologies at design time. Core principles include least-privilege isolation, strict separation of trusted and untrusted data, continuous monitoring, and validation of URLs and high-risk actions. Joint guidance issued in April 2026 by Canada, Australia, the United States, New Zealand, and the United Kingdom covers the full lifecycle from design through operation.
