The Hidden Dangers of Outdated Software: A Cyber Security Perspective
- URL: http://arxiv.org/abs/2505.13922v1
- Date: Tue, 20 May 2025 04:36:29 GMT
- Title: The Hidden Dangers of Outdated Software: A Cyber Security Perspective
- Authors: Gogulakrishnan Thiyagarajan, Vinay Bist, Prabhudarshi Nayak,
- Abstract summary: Outdated software remains a potent and underappreciated menace in 2025's cybersecurity environment.<n>The article offers a detailed analysis of the nature of software vulnerabilities, the underlying reasons for user resistance to patches, and organizational barriers that compound the issue.<n>It suggests actionable solutions, including automation and awareness campaigns, to address these shortcomings.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Outdated software remains a potent and underappreciated menace in 2025's cybersecurity environment, exposing systems to a broad array of threats, including ransomware, data breaches, and operational outages that can have devastating and far-reaching impacts. This essay explores the unseen threats of cyberattacks by presenting robust statistical information, including the staggering reality that 32% of cyberattacks exploit unpatched software vulnerabilities, based on a 2025 TechTarget survey. Furthermore, it discusses real case studies, including the MOVEit breach in 2023 and the Log4Shell breach in 2021, both of which illustrate the catastrophic consequences of failing to perform software updates. The article offers a detailed analysis of the nature of software vulnerabilities, the underlying reasons for user resistance to patches, and organizational barriers that compound the issue. Furthermore, it suggests actionable solutions, including automation and awareness campaigns, to address these shortcomings. Apart from this, the paper also talks of trends such as AI-driven vulnerability patching and legal consequences of non-compliance under laws like HIPAA, thus providing a futuristic outlook on how such advancements may define future defenses. Supplemented by tables like one detailing trends in vulnerability and a graph illustrating technology adoption, this report showcases the pressing demand for anticipatory update strategies to safeguard digital ecosystems against the constantly evolving threats that characterize the modern cyber landscape. As it stands, it is a very useful document for practitioners, policymakers, and researchers.
Related papers
- CyFence: Securing Cyber-Physical Controllers via Trusted Execution Environment [45.86654759872101]
Cyber-physical systems (CPSs) have experienced a significant technological evolution and increased connectivity, at the cost of greater exposure to cyber-attacks.<n>We propose CyFence, a novel architecture that improves the resilience of closed-loop control systems against cyber-attacks by adding a semantic check.<n>We evaluate CyFence considering a real-world application, consisting of an active braking digital controller, demonstrating that it can mitigate different types of attacks with a negligible overhead.
arXiv Detail & Related papers (2025-06-12T12:22:45Z) - Preventing Jailbreak Prompts as Malicious Tools for Cybercriminals: A Cyber Defense Perspective [1.083674643223243]
Jailbreak prompts pose a significant threat in AI and cybersecurity, as they are crafted to bypass ethical safeguards in large language models.
This paper analyzes jailbreak prompts from a cyber defense perspective, exploring techniques like prompt injection and context manipulation.
We propose strategies involving advanced prompt analysis, dynamic safety protocols, and continuous model fine-tuning to strengthen AI resilience.
arXiv Detail & Related papers (2024-11-25T18:23:58Z) - Countering Autonomous Cyber Threats [40.00865970939829]
Foundation Models present dual-use concerns broadly and within the cyber domain specifically.
Recent research has shown the potential for these advanced models to inform or independently execute offensive cyberspace operations.
This work evaluates several state-of-the-art FMs on their ability to compromise machines in an isolated network and investigates defensive mechanisms to defeat such AI-powered attacks.
arXiv Detail & Related papers (2024-10-23T22:46:44Z) - Is Generative AI the Next Tactical Cyber Weapon For Threat Actors? Unforeseen Implications of AI Generated Cyber Attacks [0.0]
This paper delves into the escalating threat posed by the misuse of AI, specifically through the use of Large Language Models (LLMs)
Through a series of controlled experiments, the paper demonstrates how these models can be manipulated to bypass ethical and privacy safeguards to effectively generate cyber attacks.
We also introduce Occupy AI, a customized, finetuned LLM specifically engineered to automate and execute cyberattacks.
arXiv Detail & Related papers (2024-08-23T02:56:13Z) - Safety in Graph Machine Learning: Threats and Safeguards [84.26643884225834]
Despite their societal benefits, recent research highlights significant safety concerns associated with the widespread use of Graph ML models.
Lacking safety-focused designs, these models can produce unreliable predictions, demonstrate poor generalizability, and compromise data confidentiality.
In high-stakes scenarios such as financial fraud detection, these vulnerabilities could jeopardize both individuals and society at large.
arXiv Detail & Related papers (2024-05-17T18:11:11Z) - The MESA Security Model 2.0: A Dynamic Framework for Mitigating Stealth Data Exfiltration [0.0]
Stealth Data Exfiltration is a significant cyber threat characterized by covert infiltration, extended undetectability, and unauthorized dissemination of confidential data.
Our findings reveal that conventional defense-in-depth strategies often fall short in combating these sophisticated threats.
As we navigate this complex landscape, it is crucial to anticipate potential threats and continually update our defenses.
arXiv Detail & Related papers (2024-05-17T16:14:45Z) - The New Frontier of Cybersecurity: Emerging Threats and Innovations [0.0]
The research delves into the consequences of these threats on individuals, organizations, and society at large.
The sophistication and diversity of these emerging threats necessitate a multi-layered approach to cybersecurity.
This study emphasizes the importance of implementing effective measures to mitigate these threats.
arXiv Detail & Related papers (2023-11-05T12:08:20Z) - Graph Mining for Cybersecurity: A Survey [61.505995908021525]
The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society.
Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities.
With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
arXiv Detail & Related papers (2023-04-02T08:43:03Z) - Proceedings of the Artificial Intelligence for Cyber Security (AICS)
Workshop at AAAI 2022 [55.573187938617636]
The workshop will focus on the application of AI to problems in cyber security.
Cyber systems generate large volumes of data, utilizing this effectively is beyond human capabilities.
arXiv Detail & Related papers (2022-02-28T18:27:41Z) - Adversarial Machine Learning Attacks and Defense Methods in the Cyber
Security Domain [58.30296637276011]
This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques.
It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
arXiv Detail & Related papers (2020-07-05T18:22:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.