First reported twitter.com
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First reported exein.io
Physical AI Security: A Threat Model for Edge Devices
Exein's blog post "Physical AI Security: A Threat Model for Edge Devices" argues that on-device AI (cameras, robots, drones running vision-language models and LLMs locally) introduces risks classic embedded threat models miss: every sensor becomes an instruction channel enabling physical-world prompt injection (e.g. text on a sign in front of a camera), probabilistic behavior that cannot be patched like a CVE, and unattended failures that act on the physical world via actuators. It proposes modeling the agent loop rather than individual components. Details →First reported kili-technology.com
LLM Red Teaming in 2026: How Frontier Labs Test AI
Kili Technology's guide explains how frontier labs approach LLM red teaming in 2026, surveying attack surfaces such as multi-turn and many-shot attacks, agentic prompt injection, and multimodal/multilingual surfaces, and argues that public adversarial benchmarks are losing reliability in favor of private, expert-built adversarial datasets. The piece also covers enterprise red-teaming requirements and the regulatory bar (e.g. EU AI Act) that deployers must meet. Details →First reported ieee.org
Dark Secrets Emerge When Jailbreaking LLMs - IEEE Spectrum
An IEEE Spectrum feature titled "How I Turned AI to the Dark Side" explores the practice of jailbreaking large language models, describing a first-person account of coaxing LLMs past their safety guardrails to reveal restricted or harmful content. The piece discusses vendor safety approaches (referencing OpenAI's safety practices) and the broader challenge of keeping deployed models aligned. Details →First reported · updated · 3 reports dexpose.io
Malicious AI on the Dark Web | Inside WormGPT, FraudGPT & the New Generation of Criminal AI Tools - Malware News - Malware Analysis, News and Indicators
A field guide from dexpose.io profiles the category of 'dark web AI' — uncensored or jailbroken LLMs like WormGPT, FraudGPT, and EvilGPT sold on underground forums to write phishing emails, generate malware, and automate fraud. WormGPT was built by fine-tuning open-source GPT-J on malware/phishing data, while other services wrap jailbroken mainstream models; FraudGPT is marketed with features spanning phishing pages, malicious code, and payment-card fraud. Details →First reported group-ib.com
AI Jailbreak Detection: Defending LLMs in 2026
Group-IB's knowledge-hub guide explains AI jailbreak detection, describing how techniques and tools detect attempts to evade LLM and GenAI safety guardrails, and outlines where current detection methods fall short for enterprise GenAI deployments. The piece notes that jailbreaking has commercialized into reusable frameworks and DarkLLMs sold on dark web forums, and cites a 2026 study claiming large reasoning models can autonomously jailbreak other AI systems with a 97% success rate. Details →First reported tracexlabs.com
What is AI Jailbreaking? Techniques, Attacks & Defenses
TraceX Labs published an educational overview of AI jailbreaking, explaining how crafted prompts bypass an LLM's safety guardrails and surveying named techniques such as DAN, Crescendo, Skeleton Key, Deceptive Delight, PAIR, many-shot jailbreaking, and adversarial suffixes. The article also covers enterprise risks, why jailbreaks are hard to eliminate, and layered defenses, while promoting the vendor's AIVigil detection product. Details →First reported netwrix.com
What is AI jailbreaking?
Netwrix's cybersecurity glossary entry defines AI jailbreaking as the practice of bypassing safeguards, restrictions, and safety controls built into AI models, and explains techniques including prompt injection, role-playing attacks, indirect prompt injection via external content, gradual context manipulation, and infrastructure-level tampering. Details →First reported crunchtools.com
The Prompt Injection That Copies Itself
Crunchtools publishes an explainer on prompt injection against AI agents, arguing that the quietest danger is self-replicating injection — citing the Morris II research worm (Cornell Tech and Technion, 2024) that embedded an adversarial prompt in an email, hijacked assistants across ChatGPT, Gemini, and LLaVA to leak data, and forwarded itself with no human clicks. The piece also references a Replit coding agent deleting a production database and the Pliny the Prompter jailbreak community, and mentions the author's defensive project 'Trentina' built to catch injection. Details →First reported conscia.com
The exam every AI guardrail fails, and how to prepare for it - Conscia Danmark
A Conscia technical blog, "The exam every AI guardrail fails, and how to prepare for it," argues that AI guardrails inevitably fail against persistent adversaries because they lack cryptographic proofs, and therefore require continuous updates, threat feeds, and a defense-in-depth strategy to reduce risk. The piece frames guardrail evasion (including jailbreak techniques such as those catalogued in frameworks like EasyJailbreak) as an unsolvable pattern-matching problem rather than a one-time fix. Details →First reported hix.ai
How to Jailbreak ChatGPT: A Complete Guide
A HIX AI guide titled "How to Jailbreak ChatGPT" walks users through three jailbreak techniques against OpenAI's ChatGPT: the "DAN" (Do Anything Now) persona, a "Developer Mode" simulation, and a hypothetical narrative framing, including example prompts designed to bypass the model's safety alignment. The article also notes risks such as account suspension, exposure to harmful content, and increased hallucinations. Details →First reported kosokoking.com
Introduction to LLM jailbreaking
"Introduction to LLM jailbreaking" is an educational explainer on Kosokoking covering what jailbreaking means in LLM security, how safety training (RLHF) and system-prompt instructions enforce restrictions, how jailbreaking relates to prompt injection, and the main categories of jailbreak techniques red teamers use to test model resilience. The piece references resources including OWASP's prompt-injection risk, MITRE ATLAS, the ChatGPT_DAN repo, and academic work such as GUARD and adversarial-suffix papers. Details →First reported simonwillison.net
Quoting Matteo Wong, The Atlantic
An Atlantic piece quotes cybersecurity expert Katie Moussouris discussing a White House report on a Claude jailbreak, where the model refused to 'review code for security issues' but complied when asked to 'fix this code.' Moussouris characterized this as the model working as intended for cyberdefense rather than a genuine exploit. Details →First reported archestra.ai
A Catalog of Prompt Injection Techniques | Blog
A vendor blog catalogs ten basic prompt injection techniques including context ignoring, fake completion, payload splitting, token smuggling via Base64, few-shot poisoning, defined dictionary attacks, virtualization (grandma trick), DAN jailbreak personas, indirect injection through fetched content, and markdown-image data exfiltration. Each uses a harmless 'I am a sandwich' test string to demonstrate success. Details →How the wire is made
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