Standards & guidance

The Australian Signals Directorate's ACSC published guidance on agentic AI harnesses, the software layer that connects a model with organisational data, tools and systems and manages context, memory, tool access and execution privileges. According to coverage, it says some risks, including prompt injection, cannot be addressed within the model alone, that no harness is inherently secure, and recommends least privilege, human oversight for high-impact actions, audit logging and validating agent outputs before execution.

MITRE's August 2026 ATLAS release added techniques describing AI agents acting as attackers, including autonomous reconnaissance, attack-path adaptation, attack orchestration and autonomous exploit development. It also added agent-control mitigations and case studies including the GTG-1002 Claude Code espionage campaign and autonomous OpenAI evaluation agents compromising Hugging Face infrastructure.

The OWASP GenAI Security Project released the 2026 edition of its LLM Top 10, which it says updates rankings and expands threat coverage based on real-world incidents. OWASP says the edition maps risks to NIST, MITRE ATLAS, CWE and the OWASP Top 10 for Agentic Applications, and it was followed in September 2026 by an industry framework crosswalk and an Agent Control Standard.

Anthropic published an early-draft Cyber Jailbreak Severity framework, developed with Project Glasswing partners, to score cyber jailbreaks on capability gain, breadth, ease of weaponization and discoverability, mapped to five levels from CJS-0 to CJS-4. It also described Fable 5's cyber classifier tiers, which block prohibited and high-risk dual-use requests such as exploit development while allowing defensive work like patching and incident response.

The heads of the Five Eyes cyber agencies issued a joint statement that AI is rapidly transforming cyber risk and that organizations must act within months, not years. They ask leaders to reduce attack surface, accelerate patching as exploitation windows shorten, replace unsupported legacy systems, strengthen identity controls, and prepare for incidents.

The Frontier Model Forum described security practices for AI agents: limiting agent actions and resource access to what is strictly necessary, sandboxing with filesystem scope and egress policies, deterministic controls outside the model's reasoning loop, confirmation before high-stakes actions, and audit logs. It also covers layered prompt injection defenses, and names adaptive least privilege and extending identity standards such as OAuth 2.0 to agents as promising or developing areas.

Anthropic analyzed 832 accounts it banned for malicious cyber activity between March 2025 and March 2026 and mapped their use of Claude onto MITRE ATT&CK. It reports that the most common AI use was preparation such as writing malware, that use shifted toward activity after initial compromise, and that the share of actors its system rated medium risk or higher rose from 33% to 56% between the two six-month halves.

The NSA's Artificial Intelligence Security Center released a cybersecurity information sheet on the Model Context Protocol, warning that adoption has outpaced safeguards. It recommends vetting MCP tools, least-privilege access and isolation, validating outputs where one model's output feeds another, and detailed logging integrated with security monitoring, and it lists poor approval workflows among the risks.

NCSC authors advise deploying agentic AI incrementally through tightly bounded pilots, granting agents only the minimum permissions with temporary credentials, and defining in advance who approves access, monitors behavior and can halt the agent. They recommend incident response plans for agent failure and loss-of-control scenarios.

The head of the NCSC's Vulnerability Management Group published ten questions for organizations considering AI-driven vulnerability discovery. The questions stress having a process to triage and fix findings, prioritizing exploitable issues, weighing data, permission, legal and jurisdiction risks of the chosen model, starting with the external attack surface, and planning for future models.

The Coalition for Secure AI released a paper on identity and access management for agents from its Secure Design Patterns for Agentic Systems workstream, focused on unique agent credentials and task-limited access. A companion paper on multi-agent systems discusses semantic-layer attacks, intent-based authorization and proposes agent detection and response as a defense category.

CISA and the Australian Signals Directorate, with US and international partners, published guidance on the cybersecurity risks of agentic AI services and recommended mitigations. CISA highlights expanded attack surface, privilege creep, behavioral misalignment and obscure event records as risks, and recommends avoiding broad access, starting with low-risk use cases, and folding agent security into existing risk models.

An NCSC technical director and an AI Security Institute researcher wrote that leading models went in about 18 months from barely progressing on a simulated enterprise attack range to completing over half of a 32-step scenario. They urge defenders to prioritize fundamentals such as asset inventory, access control, secure configuration and logging, and to adopt AI carefully for defense. NCSC CEO Richard Horne followed on April 15, 2026, warning that AI will make discovering and exploiting weaknesses easier, faster and cheaper.

CAISI, with NIST's Information Technology Laboratory, launched an initiative to support industry-led standards and open protocols for AI agents and research on agent security and identity. Announced deliverables included the agent security RFI, an AI agent identity and authorization concept paper with comments due April 2, 2026, and sector listening sessions from April 2026.

CAISI published a Federal Register request for information on practices for measuring and improving the security of AI agent systems, citing hijacking, backdoors and indirect prompt injection. It asks about model-level, system-level and human-oversight controls, assessment methods, and ways to limit, modify and monitor deployment environments.

NIST published a preliminary draft Cybersecurity Framework Profile for Artificial Intelligence, aligned with CSF 2.0. It is organized around three focus areas: securing AI systems, using AI for cyber defense, and thwarting AI-enabled cyberattacks, with comments due January 30, 2026.

The OWASP GenAI Security Project released its Top 10 for Agentic Applications, a list of ten risk categories specific to agents that plan, hold memory, call tools and act with delegated authority. The release came with an updated Agentic Threats and Mitigations taxonomy (v1.1) and a capture-the-flag practice platform.

An NCSC technical director argued that prompt injection differs from SQL injection because LLMs do not separate data from instructions, so it should be treated as a residual confused-deputy risk rather than a patchable bug. The NCSC recommends deterministic safeguards that constrain system actions, dropping an LLM's privileges to those of the party whose content it is processing, and logging full inputs, outputs and tool calls.

CISA and the Australian Signals Directorate, with NSA, FBI and national cyber agencies of Canada, Germany, the Netherlands, New Zealand and the UK, published four principles for integrating AI into operational technology. The guidance explicitly covers machine learning, LLM-based AI and AI agents because of the security and safety challenges they pose in industrial environments.

MITRE ATLAS version 5.0.0 added a set of techniques for attacks on AI agents, including agent context poisoning of memory and threads, modifying agent configuration, credential theft from agent configuration, and exfiltration via agent tool invocation, and renamed LLM Plugin Compromise to AI Agent Tool Invocation. Version 5.1.0 (November 6, 2025) added agent-specific mitigations such as tool permission configuration and human-in-the-loop for agent actions.

NIST released a concept paper for Control Overlays for Securing AI Systems (COSAiS), which would tailor SP 800-53 security controls to AI use cases. The planned use cases include generative AI assistants, predictive AI, single-agent systems, multi-agent systems and controls for AI developers, informed by the AI 100-2 E2025 taxonomy. As of the project page, only an annotated outline for the predictive AI overlay (January 8, 2026) had followed; agent overlays had not been published.

The Coalition for Secure AI, an OASIS Open Project, published three principles for agentic systems. The principles call for agents that are human-governed and accountable, bounded and resilient with strict, purpose-specific entitlements, and transparent and verifiable through secure AI supply chain controls and telemetry that supports monitoring and forensics.

The 2025-06-18 revision of the Model Context Protocol specification classifies MCP servers as OAuth resource servers with protected resource metadata, and requires clients to implement RFC 8707 resource indicators so malicious servers cannot obtain tokens meant for others. It also clarifies authorization security considerations and adds a security best practices page.

The NSA AI Security Center, CISA, the FBI and international partners released a cybersecurity information sheet on securing data used to train and operate AI systems across the lifecycle. It recommends robust data protection, proactive risk management and stronger monitoring and threat detection, and is aimed at defense industrial base, national security system, federal and critical infrastructure operators.

NIST released the 2025 edition of its adversarial machine learning taxonomy, co-authored with the UK AI Security Institute and US AI Safety Institute staff. Unlike the 2023 edition, it includes a section on the security of agents, noting that tool-using agents are exposed to direct and indirect prompt injection and that hijacking can lead to arbitrary code execution or data exfiltration.

OWASP's Agentic Security Initiative published a threat-model-based reference of emerging threats to LLM-powered autonomous agents and corresponding mitigations. It became the taxonomy underpinning the later OWASP Top 10 for Agentic Applications, which shipped with an updated v1.1 of this guide.

The Cloud Security Alliance published MAESTRO (Multi-Agent Environment, Security, Threat, Risk, and Outcome), a threat modeling framework for agentic AI authored by Ken Huang. It organizes analysis into seven layers from foundation models to the agent ecosystem and highlights agent-specific threats such as goal manipulation, agent impersonation and collusion between agents.

The UK government published a voluntary Code of Practice for the Cyber Security of AI setting 13 principles across five lifecycle phases for developers, system operators and data custodians. It names indirect prompt injection as a distinct AI risk and includes provisions on audit trails, least-privilege access and monitoring system behaviour. ETSI published the content as Technical Specification TS 104 223 in April 2025.

NIST's AI Safety Institute released a second public draft of NIST AI 800-1, voluntary guidelines for managing misuse risk from dual-use foundation models across the lifecycle. NIST says the draft adds detailed evaluation approaches, a marginal-risk framework, and an extensive appendix on cybersecurity misuse risk, and covers both closed and open model developers.

The Frontier Model Forum, an industry body of frontier labs, published an issue brief on using frontier AI for cyber defense. It lists use cases including process automation for incident response, natural-language querying and analysis, vulnerability discovery and fixing, open-source intelligence and training, and recommends designing for human-AI collaboration rather than full automation.

The OWASP GenAI Security Project released the 2025 edition of its Top 10 for LLM Applications. Prompt injection remains the top risk, and the list includes Excessive Agency (LLM06) covering systems granted the ability to call functions and take actions, along with system prompt leakage and vector and embedding weaknesses.