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Executive Summary:

In the recent advisory the Indian Computer Emergency Response Team (CERT-In) has released a high severity warning in the older versions of the software across Apple devices. This high severity rating is because of the multiple vulnerabilities reported in Apple products which could allow the attacker to unfold the sensitive information, and execute arbitrary code on the targeted system. This warning is extremely useful to remind of the necessity to have the software up to date to prevent threats of a cybernature. It is important to update the software to the latest versions and cyber hygiene practices.

Devices Affected:

CERT-In advisory highlights significant risks associated with outdated software on the following Apple devices:

• iPhones and iPads: iOS versions that are below 18 and the 17.7 release.
• Mac Computers: All macOS builds before 14.7 (20G71), 13.7 (20H34), and  earlier 20.2 for Sonoma, Ventura, Sequoia, respectively.
• Apple Watches: watchOS versions prior to 11
• Apple TVs: tvOS versions prior to 18
• Safari Browsers: versions prior to 18
• Xcode: versions prior to 16
• visionOS: versions prior to 2

Details of the Vulnerabilities:

The vulnerabilities discovered in these Apple products could potentially allow attackers to perform the following malicious activities:

1. Access sensitive information: The attackers could easily access the sensitive information stored in other parts of the violated gadgets.
2. Execute arbitrary code: The web page could be compromised with malcode and run on the targeted system which in the worst scenario would give the intruder full Administrator privileges on the device.
3. Bypass security restrictions: Measures agreed to safeguard the device and information contained on it may be easily bypassed and the system left open to more proliferation.
4. Cause denial-of-service (DoS) attacks: The vulnerabilities could be used to cause the targeted device or service to be unavailable to the rightful users.
5. Perform spoofing attacks: There could be a situation where the attackers created fake entities or users or accounts to have a way into important information or do other unauthorized activities.
6. Elevate privileges: It is also stated that weaknesses might be exploited to authorize the attacker a higher level of privileges in the system they are targets.
7. Engage in cross-site scripting (XSS) attacks: Some of them make the associated Web applications/sites prone to XSS attacks by injecting hostile scripts into Web page code.

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Vulnerabilities:

CVE-2023-42824

• Attack vector could allow a local attacker to elevate their privileges and potentially execute arbitrary code.

Affected System

• Apple's iOS and iPadOS software

CVE-2023-42916

• To improve the out of bounds read it was mitigated with improved input validation which was resolved later.

Affected System

• Safari, iOS, iPadOS, macOS, and Apple Watch Series 4 and later devices running watchOS 10.2

CVE-2023-42917

• leads to arbitrary code execution, and there have been reports of it being exploited in earlier versions of iOS.

Affected System

• Apple's Safari browser, iOS, iPadOS, and macOS Sonoma systems

Recommended Actions for Users:

To mitigate these risks, that users take immediate action:

• Update Software: Ensure all your devices are on the most current version of the operating systems they use. Repetitive updates have important security updates that fix identified weaknesses or flaws within the system.
• Monitor Device Activity: Stay vigilant if something doesn’t seem right; if your gadgets are accessed by someone who isn’t you.
• Always use strong, distinct passwords and use two-factor authentication.
• Install and update the antivirus and Firewall softwares.
• Avoid downloading any applications or clicking   link from unknown sources 

Conclusion:

The advisory from CERT-In, clearly demonstrates the fundamental need of keeping the software on all Apple devices up to date. Consumers need to act right away to patch their devices and apply best security measures like using multiple factors for login and system scanning.  This advisory has come out when Apple has just released new products into the market such as the iPhone 16 series in India. When consumers embrace new technologies it is important for them to observe relevant measures of security precautions. Maintaining good cyber hygiene is a critical process for the protection against new threats.

Reference:

• https://www.cert-in.org.in/s2cMainServlet?pageid=PUBVLNOTES02&VLCODE=CIAD-2023-0043
• https://www.cve.org/CVERecord?id=CVE-2023-42916
• https://www.cve.org/CVERecord?id=CVE-2023-42917
• https://www.bizzbuzz.news/technology/gadjets/cert-in-issues-advisory-on-vulnerabilities-affecting-iphones-ipads-and-macs-1337253#google_vignette
• https://www.wionews.com/videos/india-warns-apple-users-of-high-severity-security-risks-in-older-software-761396

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Introduction

Emerging technologies in the digital era have made their inroads in manifold domains and locations, including the “Aviation industry”. A 2022 Cranfield University and Inmarsat report has made the point for digitalization powering a reviving age for the aviation industry. Several airport authorities are presently mobilizing power of emerging technologies such as Artificial Intelligence (AI) across the airport bedrock to provide travelers with a plain sailing and expeditious air travel experience.

The Perils of Juice-Jacking

Today, Universal Serial Bus (USB) charging ports are ubiquitous and a convenient way for travelers to keep their devices powered up. In their busy, mundane lives, people use the public charging facility while travelling. However, cybersecurity experts have warned that charging in public areas could wipe off data from an electronic device or install malware, and they have urged people to stay away from USB charging ports at airports and other public areas. This leads to the possibility that fraudsters may manipulate susceptible users via juice jacking.

Investigative journalist Brian Krebs in 2011 coined the term "Juice Jacking". It isa form of cyber attack where a public USB charging port is fiddled with and infected using hardware and software changes to pocket data or install malware on devices connected to it. The term “juice jacking” is a slang representation for electric power or energy, and “hijacking” indicates an unauthorized key toa device.

While the preliminary purpose of juice jacking is usually to pilfer sensitive information from corresponding devices, such as passwords and payment card details, attackers can exploit this stolen information to attain unauthorized to your financial accounts. If the adversary attacker installs malware in the electronic device during the juice jacking strategy, the attacker may further observe the individual's movements even after one has disconnected the device from the USB port. However, the hazards of Juice Jacking include malware infection, data heist, economic loss and damage to the reputation of an individual.

RedFlags from Agencies

In2023, the Federal Bureau of Investigation (FBI) forewarned travelers against using charging stations in public zones such as hotels, airports, and shopping malls due to malicious actors attempting to use the public USB to introduce monitoring software and malware into devices. The U.S. Federal Communications Commission (FCC) has also administered a new advisory regarding “juice jacking "and its possibility of launching a hushed cyber attack against a mobile gadget while one is charging the phone with a USB cord. Similarly, according to new research from International Business Machines (IBM) Security, many nation-state hackers are currently training their eyes on travelers.

RBI Advisory

Recently in 2024, The Reserve Bank of India (RBI) has likewise administered a warning statement to mobile phone users urging them against charging their devices using public ports. RBI has additionally accentuated the importance of safeguarding private and financial data while using mobile devices. Juice jacking is further cited as one of the scams in the RBI booklet on the modus operandi of financial fraudsters in the financial space.

Preventing juice jacking attacks

The routes to avoid Juice Jacking are to keep a tab on the USB devices, not use the public charging ports, update the phone software regularly, enable and utilize the software security measures of the device, use a USB pass-through device, a wall outlet, or a backup battery; never use unknown charging cables and use only the trusted security apps. It is further important to avoid using cables that are left behind by other travelers in any public space. Users can correspondingly turn off their devices before connecting to a wary charging port. Nevertheless, the absence of documented cases does not necessarily imply that users cannot be a target of such an attack and a warning is still recommended when securing personal gadgets with susceptible user data while using standard cables. Also, using a virtual private network (VPN) and assuring that devices have the updated security updates established can aid in mitigating the danger of cyber attacks. It is equally important to utilize the security features of your device, such as passcodes, fingerprints, or facial recognition, enabled to count as a supplementary layer of safeguard.

Conclusion

In the contemporary digital age, individuals, on the whole, need to be vigilant about “Cybersecurity hygiene” and avoid accessing susceptible data or conducting financial transactions on unsecured networks. Mobile phones or devices should run on the latest operating system, and antivirus software should be revamped to mitigate conceivable security susceptibilities.

References

• https://www.forbes.com/sites/suzannerowankelleher/2023/04/20/juice-jacking-malware-phone-airports-hotels/?sh=47adab7e82ed
• https://www.businessairportinternational.com/features/how-ai-is-improving-business-aviation-operations.html
• https://www.news18.com/business/juice-jacking-attack-scam-bank-frauds-india-8412037.html
• https://www.comparitech.com/blog/information-security/juice-jacking/
• https://blogs.blackberry.com/en/2023/04/juice-jacking-advisory
• https://www.thehindubusinessline.com/info-tech/juice-jacking-rbi-issues-warning-against-charging-mobile-phones-using-public-ports/article67895091.ece
• https://www.thehindu.com/sci-tech/technology/juice-jacking-how-hackers-target-smartphones-tethered-to-public-charging-points/article67026433.ece
• https://www.forbes.com/sites/suzannerowankelleher/2019/05/21/why-you-should-never-use-airport-usb-charging-stations/?sh=630f026a5955
• https://edition.cnn.com/2023/04/12/tech/fbi-public-charging-port-warning/index.html
• https://social-innovation.hitachi/en-in/knowledge-hub/hitachi-voice/digital-transformation/
• https://www.inmarsat.com/en/insights/aviation/2022/future-aviation-connectivity.html

The Expanding Governance Challenge of Artificial Intelligence
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Artificial intelligence (AI) systems are increasingly embedded in economic and social infrastructure. They are being adopted in financial services, healthcare diagnostics, hiring systems, and public administration. But while these systems improve efficiency and decision-making, they also introduce new forms of technological risk. 

Unlike conventional software, AI systems learn patterns from data and continue to evolve as they run. This poses governance issues since risks can arise throughout the AI life cycle, whether at the coding level or in their implementation.

The latest regulatory frameworks, such as the European Union’s AI Act (EU AI Act) and the UNESCO Recommendation on the Ethics of Artificial Intelligence, note that responsible AI governance depends on the realisation of where risks emerge across the development process. 

This article maps the AI system lifecycle, identifies the risks that emerge at each stage and evaluates the policy tools used to mitigate them using the lifecycle framework developed by the Organisation of Economic Co-operation and Development (OECD).
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The Lifecycle of an AI System
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AI systems are developed through a structured process that includes problem definition, dataset collection and preparation, model development, testing and validation, deployment, and monitoring.

The OECD conceptualises this development process as the AI system lifecycle. Each stage entails various technical and administrative procedures, since choices made during these stages will dictate the goals and limits of an AI system. Further, the quality and representativeness of training sets will have a strong effect on the behaviour of models after implementation.

Since this is an iterative and not a linear procedure, risks can be introduced at each stage of the AI lifecycle. New data can be retrained into different models, and systems are regularly updated once they have been deployed, to address performance degradation, model errors, or unintended outputs. This iterative process means governance must address risks across the entire lifecycle, not just at deployment.

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Where AI Risks Emerge
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AI risks usually emerge earlier in the development process, especially in the phases when system objectives are formulated and training data are chosen. The EU AI Act and the UNESCO Recommendation on the Ethics of AI outline the following risks:  bias and discrimination, privacy and data security violations, the absence of transparency in automated decision-making, and risks to fundamental rights.

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AI Governance Risk Landscape: Core Risk Categories Under International Frameworks
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 Risk categories jointly identified by the EU AI Act and UNESCO Recommendation on the Ethics of Artificial Intelligence

Outlining the risks throughout the AI lifecycle helps understand the areas where governance interventions are most necessary. For example, discriminatory outcomes often result from biased or unrepresentative training data, while safety failures are typically linked to inadequate testing before deployment. Risks such as misinformation arise post the development process, when generative AI systems are deployed at scale on digital platforms. 
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AI System Lifecycle: Key Risks at Each Stage
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Risks identified per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Understanding where risks emerge across the lifecycle explains why governance frameworks classify AI systems by risk and apply oversight at multiple stages.
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Policy Tools for Mitigating AI Risks
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Governments and international organisations have developed regulatory tools to help mitigate AI risks in the lifecycle. These tools are meant to make sure that AI technologies are identified as up to standard in safety, accountability and fairness prior to and after deployment. 

For example, the OECD AI Policy Observatory recommends that governments adopt policy instruments such as risk evaluations, algorithmic auditing necessities, regulatory sandboxes, and transparency necessities of AI systems. The European Union’s Artificial Intelligence Act (AI Act) is one of the most comprehensive systems of governance that introduces a risk-oriented regulation strategy. It mandates adherence to requirements concerning data governance, documentation, human oversight, and robustness, and cybersecurity. Such requirements bring regulatory checkpoints to the lifecycle of AI systems.

Mapping these policy tools across the lifecycle illustrates how governance mechanisms can intervene at different stages of AI development. 
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Governance Overlay: Policy Interventions Across the AI Lifecycle
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Regulatory tools mapped at each stage of AI development per the EU AI Act and UNESCO Recommendation on the Ethics of AI

Several policy tools are directed at the risks that occur in the pre-developmental stages. In one example, algorithmic impact assessment has been applied in various jurisdictions to measure the possible consequences of automated decision systems on society before implementation. On the same note, the requirements of dataset documentation, including dataset transparency requirements and model cards, are aimed at enhancing accountability during the training and development stages of the AI systems. Therefore, lifecycle-based policy design allows regulators to intervene before harmful outcomes occur, rather than responding only after AI systems have caused damage in real-world environments.
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The Policy Gap in AI Governance
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The misalignment between risks and governance tools across the AI lifecycle indicates a critical structural gap in existing regulations. Numerous governance processes become activated after AI systems are classified as “high risk” or after they are implemented in the real world. But the most serious sources of damage have their roots in earlier stages of the development procedure.

An example is that prejudiced or unbalanced training data is almost inevitably a source of discriminative results in automated decision systems. When these types of models are applied in areas like staffing, credit rating, or in providing services to the public, such biases can quickly spread to large populations and undermine democratic rights. In the same way, the lack of transparency in model design might result in the fact that the regulator or individuals are affected by the decision-making process. This reflects a broader timing gap in AI governance, where risks originate during design and development, but regulatory intervention typically occurs only after deployment. 

Analysis
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1. Key risks originate before deployment: As depicted in the lifecycle mapping, the data collection and model development phase presents several significant governance risks as opposed to the deployment phase. Structural issues can be entrenched within AI systems even before they are deployed in practice due to bias in data sets, incomplete reporting of training sets, and obscured network designs.

2. Data governance is a primary point of vulnerability: Most of the instances of algorithmic discrimination listed above are associated with training material that is not representative of some population groups or is historical. Since machine learning models are optimisations of patterns that exist in datasets, these biases can be carried through the whole lifecycle and reproduced after deployment. 

3. Regulatory approaches remain mismatched across jurisdictions: Different countries adopt varying approaches to AI governance, ranging from risk-based frameworks such as the EU AI Act to more sector-specific or voluntary guidelines in other regions. This divergence creates inconsistencies in safety, accountability, and enforcement standards, allowing risks to persist across borders and potentially undermining the protection of users in globally deployed AI systems. 

4. Governance interventions remain uneven across the lifecycle: Whereas the various regulatory instruments aim at deployment and monitoring, fewer instruments systematically tackle the risks that are posed by the previous design and development phases.
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Recommendations
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1. Introduce mandatory lifecycle risk assessments: The regulatory systems need to demand systemic risk evaluation at the beginning of AI development, especially at the problem design and dataset selection phases. This would assist in detecting possible harmful applications in advance, before systems are constructed and installed.

2. Strengthen dataset governance standards: Training datasets must be supplemented with documentation as to their provenance, composition and limitations. Standardised documentation frameworks of data sets can assist in the discovery by regulators and auditors of the potential sources of bias or privacy threats.

3. Expand independent algorithmic auditing: AI systems can be assessed by regular third-party audits based on fairness, strength, and security weaknesses. The auditing mechanisms especially apply to high-risk systems employed in employment, finance or the public services.

4. Integrate continuous monitoring requirements: AI systems may be monitored regularly after implementation to identify model drift, unforeseen consequences, or abuse. Reporting systems can facilitate the process where the regulators can see the emerging risks and modify the governance systems.
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Conclusion - The Need for Global AI Governance
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Despite growing regulatory attention, global air governance remains fragmented. Different jurisdictions adopt varying approaches to risk classification, oversight, and enforcement, leading to inconsistencies in safety and accountability standards. Given that AI systems are often developed, deployed, and used across borders, this lack of coordination allows risks to persist beyond national regulatory frameworks.

Addressing these challenges requires a shift towards greater international cooperation and lifecycle-based governance. Developing shared standards, improving cross-border regulatory alignment, and embedding oversight across all stages of AI development will be essential to ensuring that AI systems are safe, transparent, and accountable in a globally interconnected environment.
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References
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• OECD AI lifecycle
• OECD AI system lifecycle description
• OECD AI governance lifecycle framework
• EU AI Act overview
• EU AI Act risk categories
• UNESCO Recommendation on the Ethics of AI
• AI governance lifecycle analysis
• OECD AI policy tools database

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