Microsoft: 5 Generative AI Security Threats Organizations Must Address
Category:Research & Analysis
Microsoft published analysis of critical GenAI threats facing organizations. What's concerning: 66% of orgs are developing custom GenAI apps, 88% worry about indirect prompt injection, 80% cite data leakage as top concern. Russia, China, Iran, and North Korea have doubled their use of AI to mount cyberattacks and spread disinfo—translating phishing emails to fluent English, generating deepfake executive videos, and creating adaptive malware that evades detection in real time. The five threat categories: (1) poisoning attacks manipulating training data to skew outputs, (2) evasion attacks using obfuscation/jailbreak prompts to bypass filters, (3) prompt injection attacks overriding original instructions to steer models toward malicious actions, (4) unpredictable model behavior making it difficult to anticipate responses to malicious input, (5) cloud vulnerabilities where attackers exploit weaknesses in model, app, or infrastructure to move laterally. Microsoft Defender for Cloud provides end-to-end AI security—scans code repos, monitors containers, maps attack paths, detects jailbreak attacks, credential theft, and data leakage using 100T+ daily signals.
CORTEX Protocol Intelligence Assessment
This highlights the dual-use nature of AI—the same capabilities organizations deploy for defense enable sophisticated attacks. Prompt injection and poisoning represent fundamentally new vulnerability classes that traditional security controls don't address. The unpredictability issue is architectural: GenAI models by design produce variable outputs, making defensive testing incomplete.
Strategic Intelligence Guidance
- Threat modeling: map where GenAI is exposed to untrusted input (user prompts, training data, external content)—these are high-risk injection points.
- Input validation inadequate: traditional allowlist/blocklist approaches fail against adversarial prompting—implement semantic analysis and output filtering.
- Data governance critical: GenAI systems with access to sensitive data must enforce least-privilege access, maintain audit logs, and detect exfiltration attempts.
- Monitor for abuse: track unusual query patterns, jailbreak attempt indicators, excessive API usage, and outputs containing sensitive information.
- Secure development: build AI security into SDLC—code scanning, dependency management, continuous monitoring from development through runtime.
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Intelligence Source: The 5 generative AI security threats you need to know about detailed in new e-book | Oct 31, 2025