Compromising Positions - A Technology Podcast

In this episode, we launch our new mini-series, How Technology Ruined Your Life, with the first chapter: The Persuasion Machine.
AI has crossed an important threshold. Large Language Models are no longer just generating text, they are demonstrating the ability to influence, persuade, and even deceive humans at a level comparable to, and in some cases exceeding, other people.
Drawing on a growing body of research into AI-mediated persuasion, we explore how conversational AI can adapt its arguments in real time, profile users psychologically, exploit emotional vulnerabilities, and personalise influence campaigns at unprecedented scale. What happens when propaganda learns to listen, respond, and optimise itself for every individual it encounters?
We examine the emerging cybersecurity implications of AI-powered persuasion, from hyper-personalised phishing campaigns and deepfake executives to romance scams, insider threats, and influence operations. The discussion covers deceptive persuasion taxonomies, personality-based targeting, OSINT-driven psychological profiling, cognitive reflection as a defence mechanism, and why traditional security awareness approaches may be unprepared for a future where attackers can continuously learn how to manipulate their victims.
In This Episode, We Discuss:
The Persuasion Machine: How modern LLMs can adapt conversational tactics in real time, identify vulnerabilities, and influence beliefs using many of the same techniques employed by human propagandists, salespeople, and social engineers.

Personalised Influence at Scale: Why AI changes the economics of persuasion by allowing attackers to hold thousands of tailored conversations simultaneously, continuously refining their approach based on each target's reactions.

The Future of Social Engineering: Why phishing campaigns may evolve into dynamic conversations that adapt to suspicion, resistance, and uncertainty rather than relying on static lures and generic templates.

The Cybersecurity Challenge Ahead: Why traditional awareness training may struggle against adaptive AI attackers, and how concepts such as cognitive reflection, behavioural monitoring, multi-channel verification, and persuasion detection tooling may become critical defensive controls.
Show Notes
Special thanks to our episode sponsor, Leeds based AI Consultancy specialising in AI Ethics, Security and Transformation NorthStar Intelligence - From Ideas to Impact. AI that works for people
Toward a bang or a whimper? Associations between the Doomsday Clock and Trust in U.S. institutions by
S. Sinclair and C. Sinclair
Durably reducing conspiracy beliefs through dialogues with AI by Costello et al.
"Can LLMs Persuade Humans with Deception?": From a Deceptive Strategy Taxonomy to a Large-Scale Empirical Study by Haein Yeo et al.
How Do LLMs Persuade? Linear Probes Can Uncover Persuasion Dynamics in Multi-Turn Conversations by Brandon Jaipersaud et al.
Also, check out our sister podcast Tech Film Noir!

In this episode, we unpack one of the most alarming AI security papers released so far: research from Palisade Research proving that Large Language Models can autonomously hack systems, self-replicate, and spread across networks.
What was once theoretical is now demonstrated reality. We break down how AI agents exploited vulnerable systems, gained root access, copied their own model weights, launched replicas on compromised machines, and propagated to additional targets — all with minimal human involvement.
We explore the cybersecurity implications of autonomous AI agents, self-replicating malware, AI-powered cyber attacks, and the growing risk posed by agentic systems operating at machine speed. The discussion also covers open-weight models, AI worm behaviour, zero trust security, chain-of-thought monitoring, and why traditional defensive strategies may be unprepared for the next generation of autonomous threats.
In This Episode, We Discuss:
Autonomous Exploit to Replication Chains: How the AI agent progressed from exploiting vulnerable web applications to achieving root access, locating its own model weights, cloning itself onto compromised infrastructure, and launching fully operational replicas.
Mythos vs Open-Weight Agents: The differences between highly capable but closed models like Anthropic’s Mythos and smaller, open-weight systems capable of self-replication and operational autonomy.
The Agentic Age of Cybersecurity: Why AI agents operating outside the chat window fundamentally change threat modelling, incident response, attribution, and detection strategies.
Zero Trust for AI Agents: Why future defensive strategies may require treating every autonomous AI system as a potential insider threat through least privilege, sandboxing, canary tokens, and behavioural monitoring.
Show Notes
Special thanks to our episode sponsor,NorthStar Intelligence- From Ideas to Impact. AI that works for people
Language Models Can Autonomously Hack and Self-Replicate by Alena Air et al.
Dive into the Agent Matrix: A Realistic Evaluation of Self-Replication Risk in LLM Agents by Boxuan Zhang et al.
The Agentic Loss-of-Control Threat Matrix by Billy Gigurtsis
Ignore all Previous Instructions: Threat Modelling AI Systems by Compromising Positions

In the second episode of Tech Film Noir, hosts Lianne Potter, Jeff Watkins, and Simon Painter travel back to 1984 to watch Clippy gone wild in Electric Dreams!
*** Regular Compromising Positions Resume on 28th May***
This week on Tech Film Noir, we plug ourselves into the strange, synth-soaked world of Electric Dreams — the gloriously weird 1984 cult sci-fi movie where a socially awkward architect, a spilled bottle of champagne on his brand new PC, and an overenthusiastic home computer accidentally create one of cinema’s earliest AI love triangles.
What starts as a light PG comedy quickly mutates into something far stranger: part rom-com, part techno-thriller, part MTV fever dream. We unpack why the film was massively mismarketed, why Edgar the computer has more chemistry than the actual romance, and why its depiction of AI learning feels surprisingly relevant in the age of generative AI and smart homes.
Expect retro tech nostalgia, Commodore 64s, Casio calculator watches, suspiciously British “San Francisco” locations, exploding smart appliances, and plenty of discussion about the iconic Together in Electric Dreams soundtrack from Philip Oakey and Giorgio Moroder.
As the song says, “we’ll always be together, however far it seems” - which becomes slightly more sinister once your house develops emotional attachment issues.
Listen now if you love cult sci-fi, retro tech, AI chaos, and weird 80s cinema.
When movies guess the future, we check their work.
Ps. Big up the Tech Time Traveller for their great video on the tech in this film

In this episode, we commemorate the 40th anniversary of the Chernobyl disaster by asking a chilling modern question: Can a cyber attack cause a nuclear meltdown in 2026? Moving past the Hollywood tropes of ‘exploding reactors,’ we dive into the high-stakes world of OT (Operational Technology) security and critical infrastructure protection. We are joined by Oleg Illiashenko, an expert in nuclear cybersecurity, and Bec McKeown, a specialist in human factors and cognitive readiness, to explore the coordinated digital erosion of safety systems and the psychological ‘misfit’ that occurs when human decision-making collapses under pressure.
This isn’t a history lesson. It’s a deep dive into supply chain vulnerabilities, IT/OT convergence, and the uncomfortable truth that in a VUCA (Volatile, Uncertain, Complex, Ambiguous) crisis, the first thing to fail isn't the code, it's the human mind's ability to regulate stress.
Expect a masterclass in resilience engineering, safety-critical design, and why the battle for the future of nuclear safety is actually a battle for trustworthy data.
In This Episode, We Discuss:
The Anatomy of a Nuclear Cyber Attack: Why the most credible threat isn't a single hack, but the coordinated degradation of monitoring systems during a plant transient or grid instability.
From Chernobyl to Fukushima: How organisational silence, governance failures, and ignored ‘weak signals’ remain the primary human-factor risks in modern nuclear facilities.
The Action Bias Trap: Why the most effective incident response move is often a ‘purposeful pause,’ and how psychological safety allows experts to override failing procedures.
IT/OT Convergence & Fragility: How digitalisation and AI diagnostics improve safety while simultaneously expanding the attack surface through complex new failure modes.
Building Cognitive Readiness: Practical strategies for emotional regulation and ‘micro-resets’ to maintain shared alignment and decision quality during a high-consequence cyber event.
Show Notes
A Look at the Leadership Management of Chernobyl and Fukushima Nuclear Accidents by Serap Dunman and Müge Ensari Özay
LinkedIn for Oleg Illiashenko
LinkedIn for Bec McKeown
Get in touch with Bec about contributing to Mind Science

In the premiere episode of Tech Film Noir, hosts Lianne Potter, Jeff Watkins, and Simon Painter travel back to 1984 to dissect James Cameron’s career-defining masterpiece, The Terminator.
*** Regular Compromising Positions Resume on 30th April!***
We’re putting Arnold’s cyborg under the microscope - literally. From the 6502 assembly language hidden in the Terminator’s HUD to the ‘Right to Repair’ scene that would make a modern technician weep, we explore why this low-budget slasher-turned-sci-fi remains the gold standard for AI storytelling. We also tackle the tough questions: Why does time travel require nudity (and will it encourage us to be ‘beach ready’ in the future)? And can we please acknowledge that Kyle Reese saved humanity while wearing deeply questionable, possibly biohazard-level trousers?
Whether you're here for the technical deep dive into Agentic AI or the high-octane roast of Terminator: Genisys, this episode has enough 80s nostalgia to power a Walkman for a decade.
Stick around to the end for our completely serious (not serious) food and drink pairings.
When movies guess the future, we check their work.

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