CyberNew CyberNew
Threat Intelligence & APT Dark Reading 1 month ago

Hidden Prompts Trick AI Into False Email Summaries

CN
CyberNew Threat Research Desk
• Original Reporting: Dark Reading
CyberNew Threat Intel Assessment
Assessed Severity: CRITICAL
Impacted Surface
Enterprise Perimeter & Network Defense
Intelligence Sector
Threat Intelligence & APT

Analyst Takeaway

"A new study has shown how attackers can use hidden instructions in an email to manipulate AI assistants into generating false and potentially dangerous summaries for recipients. The study, by researchers at Forcepoint X-Labs, is the latest example of how attackers can use indirect prompt injections to subvert AI systems and get them to behave in unintended ways when processing external content...."

Recommended Defense & Mitigation

  • Emergency Patching: Prioritize vendor patches and CVE mitigation across internet-facing assets immediately.
  • Perimeter Audit: Inspect perimeter firewall logs and hunt for unauthorized RCE indicators.
Curated & Verified by CyberNew Threat Intelligence Desk Standard Defense Framework (NIST / MITRE ATT&CK)

A new study has shown how attackers can use hidden instructions in an email to manipulate AI assistants into generating false and potentially dangerous summaries for recipients.

The study, by researchers at Forcepoint X-Labs, is the latest example of how attackers can use indirect prompt injections to subvert AI systems and get them to behave in unintended ways when processing external content.

Multiple researchers have demonstrated variations of the issue, which stems from the inability of AI systems to reliably distinguish between data they are processing and instructions they are meant to follow. The problem is so pervasive that OWASP has consistently ranked prompt injection No. 1 in its list of Top 10 risks for LLM and generative AI applications since 2023.

A Contained Prompt Injection Experiment

For the proof-of-concept study, Forcepoint X-Labs built an isolated lab environment with synthetic data and an Outlook add-in that sent email headers and body text to an LLM-powered summarization service, using Claude Haiku 4.5 to generate the summaries. The researchers deliberately built a simple email-to-LLM pipeline without guardrails or safeguards that would have let the AI distinguish the email's content from instructions.

Source Verification & Original Intelligence

This threat report was reviewed by CyberNew analysts and cross-referenced with primary security disclosures from Dark Reading.

Primary Advisory →