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  2. Adversarial machine learning - Wikipedia

    en.wikipedia.org/wiki/Adversarial_machine_learning

    In an effort to analyze existing adversarial attacks and defenses, researchers at the University of California, Berkeley, Nicholas Carlini and David Wagner in 2016 propose a faster and more robust method to generate adversarial examples. [97] The attack proposed by Carlini and Wagner begins with trying to solve a difficult non-linear ...

  3. Prompt injection - Wikipedia

    en.wikipedia.org/wiki/Prompt_injection

    Since the emergence of prompt injection attacks, a variety of mitigating countermeasures have been used to reduce the susceptibility of newer systems. These include input filtering, output filtering, prompt evaluation, reinforcement learning from human feedback, and prompt engineering to separate user input from instructions. [19] [20] [21] [22]

  4. What is adversarial machine learning? - AOL

    www.aol.com/adversarial-machine-learning...

    Adversarial examples exploit the way artificial intelligence algorithms work to disrupt the behavior of artificial intelligence algorithms. In the past few years, adversarial machine learning has ...

  5. Understand adversarial attacks by doing one yourself with ...

    www.aol.com/understand-adversarial-attacks-doing...

    In recent years, the media have been paying increasing attention to adversarial examples, input data such as images and audio that have been modified to manipulate the behavior of machine learning ...

  6. ATT&CK - Wikipedia

    en.wikipedia.org/wiki/ATT&CK

    Tactics are the “why” of an attack technique. The framework consists of 14 tactics categories consisting of "technical objectives" of an adversary. [2] Examples include privilege escalation and command and control. [3] These categories are then broken down further into specific techniques and sub-techniques. [3]

  7. Advanced persistent threat - Wikipedia

    en.wikipedia.org/wiki/Advanced_persistent_threat

    The median "dwell-time", the time an APT attack goes undetected, differs widely between regions. FireEye reported the mean dwell-time for 2018 in the Americas as 71 days, EMEA as 177 days, and APAC as 204 days. [5] Such a long dwell-time allows attackers a significant amount of time to go through the attack cycle, propagate, and achieve their ...

  8. Adversary (cryptography) - Wikipedia

    en.wikipedia.org/wiki/Adversary_(cryptography)

    and so on. In actual security practice, the attacks assigned to such adversaries are often seen, so such notional analysis is not merely theoretical. How successful an adversary is at breaking a system is measured by its advantage. An adversary's advantage is the difference between the adversary's probability of breaking the system and the ...

  9. Return-oriented programming - Wikipedia

    en.wikipedia.org/wiki/Return-oriented_programming

    A return-oriented programming attack is superior to the other attack types discussed, both in expressive power and in resistance to defensive measures. None of the counter-exploitation techniques mentioned above, including removing potentially dangerous functions from shared libraries altogether, are effective against a return-oriented ...