Aggressor Exploit Generator V0.85 Download ((LINK))

19 noviembre, 2022

por lasaaffl
Publicado: 19 noviembre, 2022 (2 años atrás)

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Aggressor Exploit Generator V0.85 Download ((LINK))

Aggressor Exploit Generator V0.85 Download

Nations are accumulating cyber resources in the form of stockpiles of zero-day exploits as well as other novel methods of engaging in future cyber conflict against selected targets. This paper analyzes the optimal timing for the use of such cyber resources. A simple mathematical model is offered to clarify how the timing of such a choice can depend on the stakes involved in the present situation, as well as the characteristics of the resource for exploitation. The model deals with the question of when the resource should be used given that its use today may well prevent it from being available for use later. The analysis provides concepts, theory, applications, and distinctions to promote the understanding strategy aspects of cyber conflict. Case studies include the Stuxnet attack on Iran’s nuclear program, the Iranian cyber attack on the energy firm Saudi Aramco, the persistent cyber espionage carried out by the Chinese military, and an analogous case of economic coercion by China in a dispute with Japan. The effects of the rapidly expanding market for zero-day exploits are also analyzed. The goal of the paper is to promote the understanding of this domain of cyber conflict to mitigate the harm it can do, and harness the capabilities it can provide.

Social networks play an important role in forming personal relationships, political networks, economic networks, and physical networks. They also provide the foundation of online marketing and advertising. Generally, users tend to access these networks without control, and can easily fall into the trap of unwanted, malicious, or strange programs and information that exploit their networks for selfish gains. In this paper, we present the novel uses of social networks as a new type of cybersecurity landscape, which contains an attacker-defender game and a target-oriented social network attack game. The Social Network Attack Game is a hybrid game with a target-oriented social network attack game and a snowball user-game scheme. The game model is established on data collected from a social network site. The main difference between the two-players is that the defender is a human player who tries to protect the system while the attacker is a computer program. It aims to effectively and efficiently defeat the attackers using the defender’s attack strategy. In this paper, we take malware into consideration. If the attacker uses a malicious program, it will damage the victim computer. To defend against malicious programs, we presented a defense strategy which is based on virtual-machine technology.

In this paper, we present LiMonguistic Style-Motivated backdoor attack (LISM), the first hidden trigger backdoor attack which exploits linguistic styles for backdooring NLP models. Besides the basic requirements on attack success rate and normal model performance, LISM realizes the following advanced design goals compared with previous word-based backdoor: (a) LISM weaponizes text style transfer models to learn to generate sentences with an attacker-specified linguistic style (i.e., trigger style), which largely preserves the malicious semantics of the base sentence and reveals almost no abnormality exploitable by detection algorithms. (b) Each base sentence is dynamically paraphrased to hold the trigger style, which has almost no dependence on common words or phrases and therefore evades existing defenses which exploit the strong correlation between trigger words and misclassification. Extensive evaluation on 5 popular model architectures, 3 real-world security-critical tasks, 3 trigger styles and 3 potential countermeasures strongly validates the effectiveness and the stealthiness of LISM.
While the Smart TV market is experiencing explosive growth, security researchers have been discovering multiple weaknesses in the technology. We specifically note that most of the Smart TV devices are often connected to the Internet. We particularly focus on TV devices to examine the security of connected TVs. Specifically, we identified previously unreported security vulnerabilities in the Smart TV software. We first uncovered attacks against the TV’s underlying OS kernel, which allow unauthorized device access to their Internet connections (e.g., stealing of the user’s credentials for web browsing), play music and movies on an external connected device, display pirated video and pictures on the display, and record video and pictures for later viewing. We then conducted an in-depth analysis of the TV’s ARM code to identify software weaknesses. We established the feasibility of the attacks, assessed their impact on the TV kernel, and developed proof-of-concept exploits for the identified weaknesses. With the reported implementations, we demonstrate several classes of attacks on the TV kernel, ranging from the theft of user credentials for web browsing to audio and video playback and recording. Lastly, we conducted various misuse attacks on a commercial smart TV platform. We extensively evaluated the reported flaws in their co-manufactured software and their feasibility with multiple hardware platforms and attack scenarios. We also verified the impact of the vulnerabilities on the applications running on the smart TV, which in turn affects the impact on their overall security. Together, this research provides comprehensive technical details on the previously unreported vulnerabilities in popular TV devices and establishes the feasibility of attacks against the TV software kernel.
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