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- AI in the Crosshairs: Model Abuse, Prompt Risks & Rogue Agents
AI in the Crosshairs: Model Abuse, Prompt Risks & Rogue Agents
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Prompt Injection Exploits Hit Enterprise AI Tools
Attackers are tricking internal LLMs by embedding hidden instructions within user inputs. Once executed, the model leaks sensitive data, modifies workflows, or bypasses guardrails.
Model Extraction via API Abuse
High-volume probing of AI endpoints allows adversaries to approximate your internal models. The result: reverse-engineered logic, IP theft, or adversarial clone models.
Rogue AI Agents Running Unsanctioned Tasks
Users are spinning up AI-powered bots (e.g. autonomous schedulers, data scrapers) outside oversight, letting them access files, emails, or internal APIs.
Deepfake Audio Scams Targeting Finance Teams
Short snippets of voice (as little as 5 seconds) are being used to trick finance & accounting into approving transfers or vendor changes—especially when combined with urgency.
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Credential Reuse Between AI & Business Tools
Devs are using the same credentials across AI endpoints, cloud consoles, and back‑office tools. A breach of one leaks others.
AI Audit Trails Underexpose Error Handling
Internal LLM logs often omit error states, fallback logic, or prompt history for privacy. That makes it easier to abuse edge cases without detection.
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