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Adversarial Attacks on Large Language Models and Defense Mechanisms

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Manage episode 522295605 series 3474671
HackerNoon에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 HackerNoon 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/adversarial-attacks-on-large-language-models-and-defense-mechanisms.
Comprehensive guide to LLM security threats and defenses. Learn how attackers exploit AI models and practical strategies to protect against adversarial attacks.
Check more stories related to cybersecurity at: https://hackernoon.com/c/cybersecurity. You can also check exclusive content about #adversarial-attacks, #llm-security, #defense-mechanisms, #prompt-injection, #user-preference-manipulation, #ai-and-data-breaches, #owasp, #adversarial-ai, and more.
This story was written by: @hacker87248088. Learn more about this writer by checking @hacker87248088's about page, and for more stories, please visit hackernoon.com.
Large Language Models face growing security threats from adversarial attacks including prompt injection, jailbreaks, and data poisoning. Studies show 77% of businesses experienced AI breaches, with OWASP naming prompt injection the #1 LLM threat. Attackers manipulate models to leak sensitive data, bypass safety controls, or degrade performance. Defense requires a multi-layered approach: adversarial training, input filtering, output monitoring, and system-level guards. Organizations must treat LLMs as untrusted code and implement continuous testing to minimize risks.

  continue reading

271 에피소드

Artwork
icon공유
 
Manage episode 522295605 series 3474671
HackerNoon에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 HackerNoon 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.

This story was originally published on HackerNoon at: https://hackernoon.com/adversarial-attacks-on-large-language-models-and-defense-mechanisms.
Comprehensive guide to LLM security threats and defenses. Learn how attackers exploit AI models and practical strategies to protect against adversarial attacks.
Check more stories related to cybersecurity at: https://hackernoon.com/c/cybersecurity. You can also check exclusive content about #adversarial-attacks, #llm-security, #defense-mechanisms, #prompt-injection, #user-preference-manipulation, #ai-and-data-breaches, #owasp, #adversarial-ai, and more.
This story was written by: @hacker87248088. Learn more about this writer by checking @hacker87248088's about page, and for more stories, please visit hackernoon.com.
Large Language Models face growing security threats from adversarial attacks including prompt injection, jailbreaks, and data poisoning. Studies show 77% of businesses experienced AI breaches, with OWASP naming prompt injection the #1 LLM threat. Attackers manipulate models to leak sensitive data, bypass safety controls, or degrade performance. Defense requires a multi-layered approach: adversarial training, input filtering, output monitoring, and system-level guards. Organizations must treat LLMs as untrusted code and implement continuous testing to minimize risks.

  continue reading

271 에피소드

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