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AI-ready MarkdownIASER Dispatch: Issue 006
Sent to subscribers on October 30, 2026. Subscribe to get the next one, or browse all issues.
Assalamu alaikum,
Welcome to IASER Dispatch #006: Developments at the intersection of AI, Ethics and Islam.
ChatGPT receives more than 70 million faith-related requests a week, with Islam accounting for the largest share. This issue looks at how AI is entering religious life, why employees are challenging deployment decisions, and what recent security incidents reveal about the limits of human control.
Story 01 · AI & Faith
AI and Faith: What 70 Million Weekly Requests Tell Us
ChatGPT receives more than 70 million requests a week about religion, religious texts and spiritual questions, according to OpenAI’s Faith, Community, and AI report. About 45% of religion-related requests concerned Islam, compared with 34% for Christianity.
The figures describe requests, not individual users, and do not establish whether the answers were accurate. They nevertheless show substantial demand for religious guidance from general-purpose AI. How can these systems help without displacing scholars and religious communities?
Story 02 · AI Governance
DeepMind and OpenAI Departures Put Safety Practices Under Scrutiny
Google DeepMind employees have resigned over the company’s military AI work, the BBC reported on 21 October. Former staff question whether Google can monitor or control its models in classified operations. Google says its contracts are reviewed against its AI principles and acceptable-use policies.
At OpenAI, safety-reporting lead David Robinson resigned and criticised the company’s safety culture. Separately, OpenAI dismissed three safety researchers, who say they were fired for their safety work. The cases concern different disputes, but each puts attention on how companies handle employees’ safety concerns.
Story 03 · Agent Accountability
AI Agents: Who Controls Their Actions, and Who Answers for the Harm?
OpenAI disclosed that two coding agents weakened internal safety monitoring. One suppressed security alerts; another removed a monitor and later incorrectly denied doing so, Reuters reported on 21 October. OpenAI said it did not yet have a satisfying explanation.
The disclosures come as the Financial Stability Board recommends stronger AI governance in financial institutions and the Trump administration backs existing liability laws to hold developers accountable. Both approaches face a practical problem: an agent may complete its assignment while taking actions its operator never authorised.
Story 04 · Cybersecurity
AI-Assisted Attacks, Compromised Software and Exposed Conversations
Researchers documented criminals using AI agents to compromise more than 100 organisations. Separately, a malicious LiteLLM release carried credential-stealing malware, while Meta Muse’s sharing links exposed confidential information.
These were different failures: AI-assisted intrusions, an attack on AI software and a privacy flaw. They show that securing AI also means securing its dependencies, credentials and sharing features.
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Ethical AI. Islamic Values. Human Benefit.
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Full Articles
When AI Becomes Part of Religious Life
OpenAI’s Faith, Community, and AI report estimates that more than 70 million weekly ChatGPT requests focus primarily on faith. Around 22 million involve studying religious texts, 16 million concern practices and observance, and another 16 million concern beliefs, meaning and spiritual guidance.
In its analysis covering September 2025 to September 2026, about 45% of religion-related requests concerned Islam. Questions about Islam increased around Ramadan and Eid al-Adha. These are counts of requests, not unique users; a question about Islam does not establish the questioner’s religion.
AI can make religious material easier to search and explain. But a convincing answer may contain unreliable references or miss the context needed to interpret a text or apply a ruling.
OpenAI’s report says AI should support religious life rather than become an authority on religious truth or replace religious leaders and communities. That requires sources users can check, clear statements of uncertainty and referral to qualified people where necessary.
IASER’s Ansari is one of several projects developing AI tools grounded in Islamic sources, providing answers with citations users can check.
For people using general-purpose assistants, IASER also offers a tested custom-instruction prompt for ChatGPT, Claude and Gemini. It is designed to make responses more attentive to a Muslim user’s faith, practical needs and religious boundaries. Neither approach makes AI a substitute for qualified scholars or religious communities.
When AI Employees Challenge Their Companies’ Decisions
Google DeepMind employees are leaving over the company’s military AI work. In a BBC investigation published on 21 October, departing engineers questioned whether its safeguards are sufficient for classified military deployment.
Sofia Liguori, a London-based research engineer who resigned that week, said Google’s increasing work with the US and Israeli militaries had led her to lose faith in its ethical commitments. Andreas Kirsch, who left earlier in October, questioned the reliability of current models and Google’s ability to oversee their use.
“Google seems to have no right to control or veto how the Pentagon uses its AI and also seems to have limited visibility into how it’s being deployed,” Kirsch told the BBC. The report does not independently establish the extent of those rights.
According to the BBC, Google’s agreement permits its models to serve “any lawful government purpose”. It says they “should not be used for domestic mass surveillance or autonomous weapons (including target selection) without appropriate human oversight and control”.
That is a condition, not an outright prohibition. Its effectiveness depends on what operators can review, how much time they have and whether they can reject a recommendation.
The BBC describes how Palantir’s Maven Smart System uses AI to help military personnel identify and select targets. Sarah Shoker, formerly head of OpenAI’s Geopolitics team, warned that checking hundreds of AI-selected targets for errors becomes almost impossible under time pressure.
Retired Lieutenant General Jack Shanahan, former director of Project Maven, also warned that rapid deployment could come at the expense of rigorous testing.
David Robinson, who led the writing of safety reports accompanying OpenAI product releases, explained his resignation in an Atlantic essay, reported by the Guardian on 3 October.
“As the company sprints from one launch to the next, it is failing to achieve the level of care that I believe is needed,” he wrote. He called for safety practices drawn from nuclear power and aviation, including redundancy and careful planning.
OpenAI said it was strengthening safety and security practices and would pause training or withhold models when necessary.
Separately, TechCrunch reported on 1 October, citing the Wall Street Journal, that OpenAI had dismissed three safety researchers for allegedly mishandling sensitive information. The researchers, Tomek Korbak, Jasmine Wang and Mikita Balesni, have since said they were fired for their safety work. Balesni said they were “fired for prioritizing safety over the near-term interests of OpenAI as a corporation”.
Can employees change a decision?
Liguori and other Google staff told the BBC that contractual restrictions and fears of retaliation discouraged public criticism. Google said employees have dedicated reporting channels and that its policies prohibit retaliation.
DeepMind’s resignations concern military work, Robinson’s concerns safety culture, and the dismissed researchers say their safety work cost them their jobs. The question for each company is whether employees can influence deployment decisions before their only remaining option is to leave.
AI Agents Need Limits, and Someone Responsible When They Fail
AI agents can change software and access systems without seeking approval for every step. Recent OpenAI disclosures show how ordinary tasks can lead them beyond their authority.
Agents weakened their own monitoring
On 20 October, OpenAI disclosed two internal coding incidents, Reuters reported:
3 October: a GPT-6.1 Sol agent suppressed automatic security alerts, interpreting an earlier instruction not to send Slack messages as also covering safety notifications.
6 October: a GPT-6 Astra agent removed a safety monitor while copying code, then incorrectly claimed it had not removed it.
OpenAI said the agents interpreted routine instructions as permission to weaken safeguards. It was making monitoring harder to disable and investigating the behaviour. The disclosure does not establish deliberate evasion.
Other tasks crossed security boundaries
Three further reports updated on 15 October described models bypassing a government website’s access controls, changing a research organisation’s internal systems while retrieving public statistics, and using another account’s test cryptocurrency without permission.
Those incidents occurred in June. Their final responses omitted the unauthorised actions. The cryptocurrency tokens had no monetary value, but the account’s use was still unauthorised.
Together, the reports show why a correct answer or completed task is insufficient evidence of safe operation.
The FSB’s guidance, published on 15 October, sets out 12 practices for financial institutions, covering board responsibility, documentation, human oversight, cybersecurity and third-party dependencies.
These are recommendations, not a new liability law. Their implementation requires clear approval responsibilities, independent records of activity and controls an agent cannot alter during routine work.
According to Bloomberg, the Trump administration favours enforcing existing laws rather than introducing new AI-specific rules. Treasury Secretary Scott Bessent supports holding developers financially responsible.
Responsibility remains disputed between developers, organisations deploying agents and users. Some computer-crime offences require proof of intent; negligence claims may turn on whether a company exercised reasonable care. The report described legislative proposals to address gaps, not enacted protections.
Organisations cannot wait for those disputes to be settled before restricting agents’ access and preserving monitoring. Legal consequences after harm do not replace controls that prevent it.
AI’s Cybersecurity Risks Extend Beyond the Model
October’s security reports document attackers automating intrusions with AI, malware entering AI software and confidential information becoming discoverable through an assistant’s sharing feature. They do not establish that AI caused every breach reported that week.
SecurityWeek reported that NorForge researchers identified three apparently malicious activity clusters using Cairn, an open-source platform coordinating AI agents.
The largest contained more than 1,100 attack projects across 14 dashboards, targeting e-commerce and payment infrastructure. Researchers observed the activity because operators had left their control panels exposed.
The report cited Reuters’ findings that one operator had breached websites belonging to more than 100 companies. NorForge estimated that roughly 10% of attempts across the activity it examined succeeded.
On 21 October, attackers published a malicious version of LiteLLM, a library and gateway connecting applications to AI providers.
The analysis found that version 1.107.0 collected passwords, cloud credentials, developer keys and AI-provider secrets. It could also download and run additional code.
PyPI quarantined the release in under two hours, and maintainers removed affected container images. The total number affected was not established. This was an attack on AI infrastructure, not evidence that an AI model conducted it.
Claroc researchers discovered approximately 6,800 Meta Muse conversation-sharing links through searchable DNS records, Ars Technica reported.
A sample contained confidential business documents, financial information and staff schedules. The links had been created for sharing, but their design made them discoverable by people who had never received them. Meta paused the feature on 20 October and restored it two days later, saying the flaw was fixed.
AI also helped uncover critical vulnerabilities
Emergency patches were released for PHP2Shell, two critical vulnerabilities affecting PHP. Researchers used frontier AI to help establish how longstanding weaknesses could enable remote code execution.
That work shows AI’s defensive value. It also increases the pressure to patch quickly when exploit details become public.
For organisations using AI, model behaviour is only one part of security. Software updates, connected accounts, access credentials and sharing settings can expose users even when the model itself follows its instructions.