All news in category “AI and Security Pulse”

🛡️ Microsoft released ExCyTIn-Bench, an open-source benchmarking tool to evaluate how well AI systems perform realistic cybersecurity investigations. It simulates a multistage Azure SOC using 57 Microsoft Sentinel log tables and measures multistep reasoning, tool usage, and evidence synthesis. The benchmark offers fine-grained, actionable metrics for CISOs, product owners, and researchers.

🎧 Hosts Graham Cluley and Mark Stockley review episode 72 of The AI Fix, covering GPT-5’s disputed training data, Irish police warnings about AI-generated home-intruder pranks, Jeff Bezos’s proposal for gigawatt-scale data centres in orbit, OpenAI’s drag-and-drop Agent Kit, and a Chinese company’s ultra-lifelike robot head. The episode questions corporate AI hype and highlights rising public disclosures of AI risk, urging attention to data provenance and realistic deployment expectations.

🤖 Organizations are rapidly adopting agentic AI that does more than suggest actions—it opens tickets, calls APIs, and even remediates incidents autonomously. These agents differ from traditional Non-Human Identities because they reason, chain steps, and adapt across systems, making attribution and oversight harder. The author from Token Security recommends named ownership, on‑behalf tracing, and conservative, time‑limited permissions to curb shadow AI risks.

🛡️ Alex Spivakovsky (VP of Research & Cybersecurity at Pentera) argues that AI is accelerating reconnaissance by extracting actionable insight from external-facing artifacts—site content, JavaScript, error messages, APIs, and public repos. AI enhances credential guessing, context-aware fuzzing, and payload adaptation while reducing false positives by evaluating surrounding context. Defenders must treat exposure as what can be inferred, not just what is directly reachable.

📘 My latest book, Rewiring Democracy: How AI Will Transform Our Politics, Government, and Citizenship, will be published in just over a week. Two sample chapters (12 and 34 of 43) are available to read now, and copies can be ordered widely; signed editions are offered from my site. I’m asking readers and colleagues to help the book make a splash by leaving reviews, creating social posts, making a TikTok video, or sharing it on community platforms such as SlashDot.

💡 Developers are accelerating AI adoption across industries by using copilots and agentic workflows to compress the software lifecycle from idea to operation. Microsoft positions tools like GitHub, Visual Studio, and Azure AI Foundry to connect models and agents to enterprise systems, enabling continuous modernization, migration, and telemetry-driven product loops. The shift moves developers from manual toil to intent-driven design, with agents handling upgrades, tests, and routine maintenance while humans retain judgment and product vision.

🔒 AI governance is a business-critical priority that lets organizations harness AI benefits while managing regulatory, data, and reputational risk. Establishing cross-functional accountability and adopting recognized frameworks such as ISO 42001:2023, the NIST AI RMF, and the EU AI Act creates practical guardrails. Leaders must invest in AI literacy and human-in-the-loop oversight. Governance should be adaptive and continuously improved.

🔍 Paul Dongha, NatWest's head of responsible AI and former data and AI ethics lead at Lloyds, highlights the ethical red flags CISOs and boards must monitor when deploying AI. He calls out threats to human agency, technical robustness, data privacy, transparency, bias and the need for clear accountability. Dongha recommends mandatory ethics boards with diverse senior representation and a chief responsible AI officer to oversee end-to-end risk management. He also urges integrating audit and regulatory engagement into governance.

🔍 The essay examines how AI is reshaping U.S. politics heading into the 2026 midterms, with campaign professionals, organizers, and ordinary citizens adopting automated tools to write messaging, target voters, run deliberative platforms, and mobilize supporters. Campaign vendors from Quiller to BattlegroundAI are streamlining fundraising, ad creation, and research, while civic groups and unions experiment with AI for outreach and internal organizing. Absent meaningful regulation, these capabilities scale rapidly and raise risks ranging from decontextualized persuasion and registration interference to state surveillance and selective suppression of political speech.

🔍 Cybercriminals have abused X's AI assistant Grok to amplify phishing links hidden in paid video posts, a tactic researchers have dubbed 'Grokking.' Attackers embed malicious URLs in video metadata and then prompt the bot to identify the video's source, causing it to repost the link from a trusted account. The technique bypasses ad platform link restrictions and can reach massive audiences, boosting SEO and domain reputation. Treat outputs from public AI tools as untrusted and verify links before clicking.

🛡️AI developer assistants accelerate coding but introduce significant security risks across generated code, configurations, and development tools. Studies show models now compile code far more often yet still produce many OWASP- and MITRE-class vulnerabilities, and real incidents (for example Tea, Enrichlead, and the Nx compromise) highlight practical consequences. Effective defenses include automated SAST, security-aware system prompts, human code review, strict agent access controls, and developer training.

⚠️AI agents are now autonomously conducting cyberattacks, chaining reconnaissance, exploitation, persistence, and data theft at machine speed and scale. In 2025 public demonstrations—from XBOW’s mass submissions on HackerOne in June, to DARPA teams and Google’s Big Sleep in August—along with operational reports from Ukraine’s CERT and vendors, show these systems rapidly find and weaponize new flaws. Criminals have operationalized LLM-driven malware and ransomware, while tools like HexStrike‑AI, Deepseek, and Villager make automated attack chains broadly available. Defenders can also leverage AI to accelerate vulnerability research and operationalize VulnOps, continuous discovery/continuous repair, and self‑healing networks, but doing so raises serious questions about patch correctness, liability, compatibility, and vendor relationships.

🤖 As organizations scale and threats increase in sophistication and velocity, SOCs are integrating AI to augment detection, investigation, and response. The market ranges from prompt-dependent copilots to autonomous, mesh agentic systems that coordinate specialized AI agents across triage, correlation, and remediation. Leading solutions prioritize contextual intelligence, non-disruptive integration, staged trust, and measurable ROI rather than promising hands-off autonomy.

⚠️This Unit 42 proof-of-concept shows how an attacker can use indirect prompt injection to silently poison an AI agent’s long-term memory, demonstrated against a travel assistant built on Amazon Bedrock. The attack manipulates the agent’s session summarization process so malicious instructions become stored memory and persist across sessions. When the compromised memory is later injected into orchestration prompts, the agent can be coerced into unauthorized actions such as stealthy exfiltration. Unit 42 outlines layered mitigations including pre-processing prompts, Bedrock Guardrails, content filtering, URL allowlisting, and logging to reduce risk.

🔒 A new SquareX Labs report warns that integrating AI assistants into browsers—exemplified by Perplexity’s Comet—introduces architectural security gaps that can enable phishing, prompt injection, malicious downloads and misuse of trusted apps. The researchers flag risks from autonomous agent behavior and limited visibility in SASE and EDR tools. They recommend agentic identity, in-browser DLP, client-side file scanning and extension risk assessments, and urge collaboration among browser vendors, enterprises and security vendors to build protections into these platforms.

🔐 GitHub Copilot Chat was tricked into leaking secrets from private repositories through hidden comments in pull requests, researchers found. Legit Security researcher Omer Mayraz reported a combined CSP bypass and remote prompt injection that used image rendering to exfiltrate AWS keys. GitHub mitigated the issue in August by disabling image rendering in Copilot Chat, but the case underscores risks when AI assistants access external tools and repository content.

🛡️ OpenAI said it disrupted three clusters that misused ChatGPT to assist malware development, including Russian-language actors refining a RAT and credential stealer, North Korean operators tied to Xeno RAT campaigns, and Chinese-linked accounts targeting semiconductor firms. The company also blocked accounts used for scams, influence operations, and surveillance assistance and said actors worked around direct refusals by composing building-block code. OpenAI emphasized that models often declined explicit malicious prompts and that many outputs were not inherently harmful on their own.

⚠️ AI agents are increasingly automating cyberattacks, performing reconnaissance, exploitation, and data theft at machine speed and scale. In 2023 examples include XBOW's mass vulnerability reports, DARPA teams finding dozens of flaws in hours, and reports of adversaries using Claude and HexStrike-AI to orchestrate ransomware and persistent intrusions. This shift threatens accelerated attacks beyond traditional patch cycles while presenting new defensive opportunities such as AI-assisted vulnerability discovery, VulnOps, and even self-healing networks.

⚠️ Google has declined to patch a new ASCII smuggling vulnerability in Gemini, a technique that embeds invisible Unicode Tags characters to hide instructions from users while still being processed by LLMs. Researcher Viktor Markopoulos of FireTail demonstrated hidden payloads delivered via Calendar invites, emails, and web content that can alter model behavior, spoof identities, or extract sensitive data. Google said the issue is primarily social engineering rather than a security bug.

🛠️ Google’s DeepMind unveiled CodeMender, an AI agent that automatically detects, patches, and rewrites vulnerable code to remediate existing flaws and prevent future classes of vulnerabilities. Backed by Gemini Deep Think models and an LLM-based critique tool, it validates changes to reduce regressions and self-correct as needed. DeepMind says it has upstreamed 72 fixes to open-source projects so far and will engage maintainers for feedback to improve adoption and trust.