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Showing posts with the label algorithmic bias

The Rising Threat of ToolShell: Unpacking the July 2025 SharePoint Zero-Day Exploits

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Anatomy of the ToolShell Exploit Chain Beginning around July 7, 2025, adversaries exploited a deserialization flaw in SharePoint’s on-premises service (CVE-2025-53770) to upload a malicious spinstall0.aspx payload, triggering code execution within the w3wp.exe process. A secondary path-traversal flaw (CVE-2025-53771) then enabled privilege escalation and lateral movement across corporate networks . Security researchers at Eye Security and Palo Alto Networks’ Unit 42 observed attackers bypassing identity controls – MFA and SSO – to exfiltrate machine keys, deploy persistent backdoors, and chain ransomware operations within hours of initial compromise . State-Backed Actor Involvement Microsoft attributes the campaign primarily to Storm-2603, assessed with moderate confidence to be China-based, alongside historically linked groups Linen Typhoon and Violet Typhoon . These actors have a track record of blending cyber-espionage with financially motivated ransomware like Warlock and Lo...

AI's Ethical Quandary: Navigating the Morality of Machine Decisions

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Artificial intelligence (AI) has become an integral part of modern life, revolutionizing sectors like healthcare, autonomous vehicles, and even legal systems. However, its ability to make decisions traditionally reserved for humans has sparked a profound ethical debate. This piece explores the moral and societal implications of AI decision-making, focusing on algorithmic bias, autonomous weapons, healthcare applications, and its role in the justice system. Algorithmic Bias: The Invisible Prejudice AI systems are only as unbiased as the data they are trained on. Unfortunately, many datasets reflect societal prejudices, causing AI to perpetuate or even exacerbate inequalities. For example, studies have shown that facial recognition software is less accurate in identifying people of color, leading to instances of wrongful arrests. Similarly, biased algorithms in hiring tools have disadvantaged women and minorities. To combat algorithmic bias, companies and governments must: ...