NCSC warns AI to escalate cyber threats by 2027

NCSC assesses AI will make cyber intrusions more frequent and effective by 2027, widening the attack surface and deepening a security divide between defenders.

The UK's National Cyber Security Centre has published a forward-looking assessment on how artificial intelligence will shape the cyber threat landscape through 2027. The report, building on a January 2024 baseline assessment, concludes that AI will almost certainly make cyber intrusion operations more effective and efficient across reconnaissance, vulnerability research, exploit development, social engineering, malware generation, and data exfiltration—primarily by enhancing existing TTPs rather than creating novel attack vectors. NCSC judges that only the most capable state actors will fully harness AI for advanced operations in the near term, while most other threat groups will repurpose commercial and open-source AI models to uplift their capabilities, and a proliferation of AI-enabled tools will extend intrusion capability to a wider range of state and non-state actors, including cybercriminals offering AI-enabled tools 'as a service' to novices and hacktivists.

A key concern raised is AI-assisted vulnerability research and exploit development (VRED), which is expected to shrink the already narrow window between vulnerability disclosure and exploitation, increasing risk to unpatched systems and particularly to critical national infrastructure (CNI) and its supply chains, including operational technology with weaker security. The report also highlights the growing integration of AI systems into UK technology infrastructure as an expanding attack surface, citing threats such as direct and indirect prompt injection, software vulnerabilities, and supply chain attacks against AI systems themselves, compounded by insecure data handling, weak encryption, and poor identity management.

NCSC anticipates a growing 'digital divide' between organisations that keep pace with AI-enabled defensive capability and those that fall behind, warning that keeping pace with frontier AI will be critical to cyber resilience for the coming decade. While fully automated end-to-end attacks are considered unlikely by 2027, skilled actors are expected to increasingly automate elements of the attack chain—vulnerability exploitation, malware mutation, and infrastructure changes—to evade detection, a human-machine teaming trend that will complicate threat detection and mitigation absent comparable AI-enabled defensive tools.

title: AI-Assisted Malware Generation via Command-Line Execution
description: Detects command-line execution of Python interpreters or AI model tools
  with patterns indicative of malware obfuscation or polymorphic generation - such
  as invoking LLM APIs, code mutation libraries, or obfuscation frameworks. Generalises
  on the parent/child relation and suspicious argument patterns, not specific file
  hashes or model names.
tags:
- attack.defense-evasion
- attack.t1027
logsource:
  category: process_creation
  product: windows
detection:
  selection_python_ai:
    Image|endswith:
    - \python.exe
    - \python3.exe
    CommandLine|contains:
    - obfuscate
    - mutate
    - polymorphic
    - llm
    - gpt
    - transform
    - encode
  selection_suspicious_tools:
    Image|endswith:
    - \ollama.exe
    - \transformers
  filter_legitimate_dev:
    CommandLine|contains:
    - --help
    - version
    - test
    ParentImage|endswith:
    - \git.exe
    - \devenv.exe
  condition: (selection_python_ai or selection_suspicious_tools) and not filter_legitimate_dev
falsepositives:
- Legitimate machine learning development environments using these libraries for benign
  purposes
- Data science workflows involving code transformation or encoding
level: medium
id: 2ec017f1-e9ea-5ecb-a236-0851005c047a
status: experimental
author: Vorant

title: Rapid Exploit Development and Deployment Post-Disclosure
description: Detects HTTP(S) traffic patterns indicative of AI-accelerated exploit
  development and deployment against public-facing applications - multiple malformed
  requests and payloads targeting known vulnerable endpoints within short time windows,
  characteristic of automated vulnerability scanning and exploitation chains.
tags:
- attack.initial-access
- attack.t1190
logsource:
  category: proxy
  product: network
detection:
  selection_exploit_pattern:
    http_method:
    - POST
    - PUT
    - PATCH
    uri|contains:
    - /admin
    - /api
    - /.env
    - /config
    - /backup
    - rce
    - sql
    - injection
    - xxe
    http_response_code:
    - 400
    - 403
    - 500
  selection_payload:
    body|contains:
    - exec
    - system(
    - eval(
    - shell_exec
    - passthru
    - SELECT * FROM
    - <?php
  filter_legitimate_testing:
    src_ip|startswith:
    - '10.'
    - 172.16.
    - 192.168.
    user_agent|contains:
    - curl
    - Postman
  condition: (selection_exploit_pattern and selection_payload) and not filter_legitimate_testing
falsepositives:
- Security scanning tools and vulnerability assessment platforms operated by internal
  teams
- Legitimate API testing frameworks sending various HTTP methods and payloads
level: high
id: d48fb138-2ace-5372-9a6d-4c83ad9c25ff
status: experimental
author: Vorant

title: Suspicious Email with Social Engineering via AI-Enhanced Phishing
description: Detects phishing emails with characteristics indicative of AI-assisted
  generation - patterns of linguistic anomalies, excessive urgency/threat language,
  dynamic placeholder injections, and structural inconsistencies common in LLM-generated
  content used for large-scale social engineering campaigns.
tags:
- attack.initial-access
- attack.t1566
logsource:
  category: email
  product: mail_server
detection:
  selection_urgency:
    body|contains:
    - immediately
    - urgent action required
    - within 24 hours
    - verify now
    - confirm identity
    - suspicious activity detected
  selection_ai_artifacts:
    body|contains:
    - '[USER_NAME]'
    - '[COMPANY]'
    - '[DATE]'
    - '[FIRST_NAME]'
    body|re: Best regards|Kind regards|Yours truly.*[0-9]{6}
  filter_internal:
    sender_domain|endswith:
    - '@company.local'
    - '@trusted-partner.com'
  condition: (selection_urgency and selection_ai_artifacts) and not filter_internal
falsepositives:
- Legitimate bulk marketing emails with placeholder templates
- Short-URL services used in legitimate internal communications
level: medium
id: f9a61a7e-5d86-5362-9c94-80960d153b4c
status: experimental
author: Vorant

Source reporting: https://www.ncsc.gov.uk/report/impact-ai-cyber-threat-now-2027