University of Kwazulu-Natal

Weaponized Ai A New Era Of Threats And How We Can Counter It

Bonisiwe Shabane
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weaponized ai a new era of threats and how we can counter it

Creating a healthy digital civic infrastructure ecosystem means not just deploying technology for the sake of efficiency, but thoughtfully designing tools built to enhance democratic engagement from connection to action. Last week’s leak of the U.S. Department of Education’s proposed “Compact for Academic Excellence in Higher Education” drew intense reactions across academia. Critics call it government overreach threatening free expression, while supporters see a chance for reform and renewed trust between universities and policymakers. Danielle Allen, James Bryant Conant University Professor at Harvard University, director of the Democratic Knowledge Project and the Allen Lab for Democracy Renovation, weighs in. Amid rising illiberalism, Danielle Allen urges a new agenda to renew democracy by reorienting institutions, policymaking, and civil society around the intentional sharing of power.

Creating a healthy digital civic infrastructure ecosystem means not just deploying technology for the sake of efficiency, but thoughtfully designing tools built to enhance democratic engagement from connection to action. Public engagement has long been too time-consuming and costly for governments to sustain, but AI offers tools to make participation more systematic and impactful. Our new Reboot Democracy Workshop Series replaces lectures with hands-on sessions that teach the practical “how-to’s” of AI-enhanced engagement. Together with leading practitioners and partners at InnovateUS and the Allen Lab at Harvard, we’ll explore how AI can help institutions tap the collective intelligence of our communities more efficiently and effectively. We’ve officially entered a new era of cyberattacks. November reports from Anthropic and Oligo Security detail the use of jail broken LLMs to carry out large scale cyberattacks.

In both cases, the companies claim that LLM based code generation and, in the Anthropic case, other LLM capabilities, were used in executing attack campaigns. This should not be a surprise. Researchers at Cornell University predicted we were on this path in May of this year. The reality is that LLMs are incredibly useful tools for a wide variety of tasks, some of which happen to include those that are relevant to cybersecurity. OpenAI has even released a dedicated cyber security researcher. But human history includes many examples of technologies not originally developed for war but were later weaponized.

Perhaps the most famous example is dynamite, which ironically was invented by the namesake of the Nobel Peace Prize. Many, many other examples exist: fertilizer, commercial airliners, 3D printers, drones…the list goes on and on. In the cybersecurity context, it means that LLMs have been turned into attack tools by cybercriminals and nation state threat actors. The long term implication of this is that the approaches that worked in the prior era are no longer going to work in the era of AI-generated or even just AI-assisted cyberattacks. In the prior era, attackers had to choose between going deep (high-value targets, high effort) or going broad (scripted spray-and-pray attacks). Generative AI collapses that tradeoff.

With well-crafted prompts, an attacker can now do both: create human-level attack campaigns and apply them to a large number of targets simultaneously without human intervention or ongoing direction. In the Anthropic case, the LLMs were given initial direction on targets and attack frameworks by human operators, including an approach to jail-breaking the underlying LLM (Claude code in this case) to circumvent the... From there the execution of the campaigns was largely autonomous and resulted in attacks on roughly thirty targets and a small number of successful breaches, according to Anthropic’s report. “Weaponized AI: A New Era of Threats and How We Can Counter It" AI has rapidly evolved from a tool for innovation to a potential threat that poses a challenge to global security and... In a new article, Dr. Shlomit Wagman highlights the diverse risks posed by AI and suggests a comprehensive framework to address these challenges.

The Expanding Landscape of AI-Driven Threats: The accessibility and scalability of AI have lowered the barriers for malicious actors to conduct sophisticated cyberattacks, spread misinformation, and exploit societal vulnerabilities. Key areas of concern include: - Psychological warfare and misinformation: AI-generated deepfakes and targeted disinformation campaigns can fabricate events, incite panic, and escalate international tensions before verification is possible. - Election interference: AI tools can manipulate political discourse by creating false narratives, altering public records, and misrepresenting candidates, thereby eroding trust in election processes. - Cybercrime and financial fraud: Advanced AI models enable the creation of hyper-personalized phishing campaigns and deepfakes that can evade security measures, resulting in significant economic losses. - Critical infrastructure: AI can automate the identification and exploitation of vulnerabilities in defense systems and critical infrastructure, facilitating rapid and adaptive cyberattacks that surpass human capabilities. A Global Framework for AI Safety and Security: As AI capabilities grow, so do the associated risks, ranging from misinformation and fraud to threats against national security and democratic institutions.

To tackle these challenges while encouraging innovation, we need a globally coordinated strategy: - International governance: Similar to how the Financial Action Task Force (FATF) standardizes global financial integrity, we require a comparable model... This would involve setting enforceable, cross-border safety standards, along with independent audits and consistent compliance mechanisms. - Market incentives: Despite the billions invested in AI development, safety remains underfunded. We need financial incentives and public-private partnerships to develop tools for deepfake detection, adversarial testing, and fraud prevention systems. - Public awareness: Strengthening digital literacy and public resilience is vital. We must equip society to recognize and respond to AI-driven manipulation, particularly in areas such as elections, financial scams, and impersonation.

- Regulatory action: We need global, enforceable AI regulations to close security gaps and ensure fair competition while aligning safety efforts worldwide. When a new technology emerges, we often feel the need to "reinvent the wheel." What I particularly appreciate about this article is that it highlights existing frameworks, such as the FATF, which can be... The FATF analogy is a strong and practical way to think about building global standards for AI governance. One actionable step could be to require AI risk impact assessments (AI-RIAs) for systems used in critical infrastructure, elections, or financial services etc. similar to privacy impact assessments. When tied to procurement or national funding, this can drive real accountability and push the industry toward security-by-design practices.

When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. New research from CrowdStrike confirms that hackers are exploiting AI to help them deliver more aggressive attacks in less time, with the tech also democratizing lesser-skilled hackers to more advanced code. However, besides this, they're also exploiting the same AI systems that are being used by enterprises – according to CrowdStrike, hackers are targeting the tools used to build AI agents, allowing them to gain... CrowdStrike is most worried about agentic AI systems, suggesting that they've now become a "core part of the enterprise attack surface." The security company says it observed "multiple" hackers exploiting vulnerabilities in the tools used to build AI agents, which marks a major shift from patterns of old.

Until now, humans have almost always been the primary entry point into a company, but now, CrowdStrike is worried that "autonomous workflows and non-human identities [are] the next frontier of adversary exploitation." Located on the campus of Stanford University and in Washington, DC, the Hoover Institution is the nation’s preeminent research center dedicated to generating policy ideas that promote economic prosperity, national security, and democratic governance. Hoover scholars form the Institution’s core and create breakthrough ideas aligned with our mission and ideals. What sets Hoover apart from all other policy organizations is its status as a center of scholarly excellence, its locus as a forum of scholarly discussion of public policy, and its ability to bring... Hoover Education Success Initiative | The Papers Throughout our over one-hundred-year history, our work has directly led to policies that have produced greater freedom, democracy, and opportunity in the United States and the world.

Hoover scholars offer analysis of current policy challenges and provide solutions on how America can advance freedom, peace, and prosperity. In an age where technological advancements are rapidly reshaping our world, the fusion of artificial intelligence (AI) and cyber threats has emerged as a formidable challenge. The weaponization of AI, a technology initially designed to enhance efficiency and innovation, has now be-come a double-edged sword, presenting both opportunities and risks in the realm of cybersecurity. Artificial intelligence, with its ability to analyze vast amounts of data and adapt to evolving scenarios, has proven invaluable in various industries. However, the dark side of this technology is evident as cybercriminals and state-sponsored actors increasingly leverage AI to enhance the scale and sophistication of their attacks. 1.

Automated Cyber Attacks: AI-driven automation has empowered cyber attackers to execute more efficient and widespread assaults. Automated malware, capable of adapting its tactics based on real-time analysis, can exploit vulnerabilities at an unprecedented speed, making traditional cybersecurity measures less effective. 2. Intelligent Phishing: Phishing attacks, a longstanding threat, have evolved with the incorporation of AI. Cyber-criminals employ machine learning algorithms to craft highly personalized and convincing phishing emails, bypassing traditional email security protocols. This makes it challenging for individuals and organizations to discern genuine communications from malicious ones.

3. Adversarial Machine Learning: The concept of adversarial machine learning involves manipulating AI algorithms to deceive the system. Cyber attackers can exploit vulnerabilities in AI models, leading to misclassifications and false outputs, thereby compromising the integrity of security systems that rely on AI for threat detection.

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