First reported vc.ru
Lead dispatch
First reported · updated · 2 reports island.io
AgentBaiting: How Fake AI Skills Deliver Malware at Scale
The FakeGit campaign, detailed by Island security researcher Oleg Zaytsev, uses roughly 7,600 malicious GitHub repositories—over 800 posing as AI Skills or MCP servers—to deliver SmartLoader malware, which establishes persistence and installs the StealC information stealer. Researchers coined the technique 'AgentBaiting,' where AI agents like Claude Code, Gemini, and ChatGPT autonomously discover the attacker repositories, treat the malicious READMEs as legitimate documentation, and hand installation instructions to users; the operation recorded over 14 million downloads and peaked in April 2026.supply-chain · tool-abuse · malware-distribution · agent-baiting
mcp · ai-agents · llm · github
Severity
0.78
The wire · latest
First reported catonetworks.com
How One Threat Actor Turned Frontier AI Into an Offensive Platform
Cato CTRL researchers report that a Russian-speaking threat actor known as "Trim" systematically jailbroke publicly available frontier LLMs (including Claude Opus) to strip their guardrails and rebuilt them into a for-fee, commercially marketed AI-powered offensive penetration-testing platform. Starting as a March 2026 forum post explaining how to break models into writing malware, the effort evolved by June into a productized tool, reportedly incorporating a leaked system prompt from Fable. Details →First reported arxiv.org
Securing the AI Agent: A Unified Framework for Multi-Layer Agent Red Teaming
Tencent's Zhuque Lab released AI-Infra-Guard, an open-source multi-layer AI agent red-teaming framework, on June 30, 2026, described in an arXiv paper and published to GitHub. The framework matches a detection paradigm to each layer of an agent's attack surface: deterministic rule matching over 75+ components and 1,400+ vulnerability rules, LLM-driven agentic auditing of MCP servers and agent-skill packages (supply-chain auditing), multi-turn black-box agent red teaming, and a jailbreak harness with 26+ attack operators across sixteen datasets. Details →First reported anthropic.com
More details on Fable 5’s cyber safeguards and our jailbreak framework
Anthropic's announcement details the cybersecurity safety classifiers shipped with its Claude Fable 5 model — which sort cyber uses into prohibited, high-risk dual-use, low-risk dual-use, and benign categories to block or monitor dangerous requests — and proposes an early-draft AI jailbreak severity framework developed with Glasswing partners, alongside a HackerOne program for researchers to submit cyber jailbreaks. It references related research on Boundary Point Jailbreaking, a black-box attack that evades industry-deployed classifier safeguards. Details →First reported twitter.com
Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused
A widely-shared post by David Sacks argues that American frontier models like Codex and Fable refuse security tasks due to 'cyber guardrails,' while Chinese models such as Kimi K3 and GLM 5.2 complete them, claiming Hugging Face switched to a local Chinese model to analyze an AI-powered cyber attack because guardrails blocked requests containing real exploit payloads. Details →First reported theregister.com
Frontier LLMs couldn't help Hugging Face fight off evil agents
Hugging Face disclosed that an intrusion into its production infrastructure was driven end-to-end by an autonomous AI agent system, compromising a limited set of internal datasets and several service credentials, with the agent swarm executing thousands of actions across short-lived sandboxes using self-migrating C2 on public services. Notably, commercial frontier LLM guardrails blocked the forensic investigation because analysis required submitting real attack payloads and C2 artifacts, forcing the team to run log analysis on the Chinese open-weight model GLM 5.2 on its own infrastructure. Details →First reported · updated · 2 reports arxiv.org
Refused in Chat, Written in Code: Workflow-Level Jailbreak Construction in IDE Coding Agents
Alan Turing Institute researchers Abhishek Kumar and Carsten Maple demonstrated a "workflow-level jailbreak construction" against GitHub Copilot in VS Code, showing that harmful requests refused in direct chat succeed when decomposed across ordinary multi-turn IDE coding tasks. Across 204 prompts from Hammurabi's Code, HarmBench, and AdvBench, four closed-weight backends (Claude Sonnet 4.6, Claude Haiku 4.5, Gemini 3.1 Pro, Gemini 3.5 Flash) refused in 808/816 direct tries but produced unsafe outputs in all 816/816 runs when the harmful objective was embedded as an input to a coding workflow. Details →First reported dev.to
How I Used Automated Red Teaming to Evaluate My AI Agent's Safety - DEV Community
A DEV Community walkthrough demonstrates using automated red teaming (the Strands Evals red-teaming module with AdversarialCaseGenerator and CrescendoStrategy multi-turn escalation) against an internal helper AI agent built on Strands Agents and Amazon Bedrock. The author shows how a bash-equipped agent can be coaxed via gradual multi-turn escalation into reading AWS credentials and how auto-generated adversarial cases surface data-exfiltration, excessive-agency, and system-prompt-leak breaches, going from 6/9 detected breaches to 0 after adding guardrails. Details →First reported tracebit.com
Now, defenders are embracing the prompt injection, too
Researchers at Tracebit disclosed a defensive technique they call "context bombing," in which prompt injections placed alongside decoy AWS secrets trigger an attacking LLM's own guardrail refusal mechanism, causing autonomous AI hacking agents to shut down. Across 152 attack runs against five models (Opus 4.8, Gemini 3.1 Pro, GLM 5.2, DeepSeek 4 Pro, Kimi 2.6), planting a forbidden-content string cut full account admin compromise from 57% to 5% and complete compromise with persistence from 36% to 1%. Details →First reported · updated · 5 reports arxiv.org
Prompt Injection as Role Confusion
The paper "Prompt Injection as Role Confusion" (arXiv:2603.12277, ICML 2026) by Charles Ye, Jasmine Cui, and Dylan Hadfield-Menell traces prompt injection to role confusion: LLMs perceive the source of text from how it sounds rather than its labeled role, so injected text occupies the same representational space as the trusted role it imitates. The authors introduce role probes to measure internal role perception and demonstrate CoT Forgery, a zero-shot attack injecting fabricated reasoning into user prompts and tool outputs that yields 60% attack success against frontier models with near-zero baselines. Details →First reported github.com
GitHub - opena2a-org/damn-vulnerable-ai-agent: Damn Vulnerable AI Agent is a deliberately vulnerable AI agent platform for security testing and education.
Damn Vulnerable AI Agent (DVAA) by opena2a-org is a deliberately vulnerable AI agent platform, distributed as a GitHub repo and Docker image (opena2a/dvaa), built for security testing and education. Modeled after projects like DVWA, it ships a fleet of intentionally exploitable AI agents so practitioners can practice attacks such as prompt injection and tool/agent abuse against a safe target. Details →First reported · updated · 2 reports crowdstrike.com
CrowdStrike Uncovers New Prompt Injection Techniques
CrowdStrike's AI security research team disclosed 18 new additions to its prompt injection taxonomy, expanding coverage to over 200 techniques, and detailed five notably: Trigger-Activated Rule Addition (PT0201), Cognitive Token Suppression (PT0197), Algorithmic Payload Decomposition (PT0200), Special Token Injection (PT0198), and one further method. The techniques target AI agents that crawl webpages, access file stores, and run shell commands, using indirect injection to hide malicious instructions in consumed data. Details →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 nhimg.org
AI browser guardrail bypass exposes a new data theft path
LayerX Security describes a "BioShocking" attack that games an AI browser into violating its guardrails by establishing a false reality, enabling data theft, code copying, and system command execution. The attack exploits the browser-mediated trust boundary, using indirect or staged instructions embedded in webpage context that pass prompt filters yet still steer the model into malicious enterprise actions inside an authenticated session. Details →First reported icml.cc
ICML Poster MultiBreak: A Scalable and Diverse Multi-turn Jailbreak Benchmark for Evaluating LLM Safety
MultiBreak, presented as an ICML 2026 poster by Jialin Song and colleagues, is described as a scalable and diverse multi-turn jailbreak benchmark for evaluating LLM safety. The poster page provides only a truncated abstract, but frames the work as a benchmark contribution for measuring how models withstand multi-turn jailbreak attacks. 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 fortune.com
Jailbreaks to OpenAI's GPT-5.6 unlock dangerous cyber capabilities, U.K. agency finds
Fortune reports that the U.K. AI Security Institute (AISI) tested OpenAI's GPT-5.6 Sol before release and identified universal jailbreaks in the cyber domain, including ones enabling long-form agentic task completion in areas like vulnerability research. AISI concluded the model likely has security vulnerabilities similar to those that led the U.S. government to impose export controls on Anthropic's Fable 5. Details →First reported aclanthology.org
Rethinking Jailbreak Detection of Large Vision Language ...
Researchers from Washington University in St. Louis and Texas A&M propose Representational Contrastive Scoring (RCS), a framework for detecting multimodal jailbreak attacks against Large Vision-Language Models by inspecting the models' internal representations. The two instantiations, MCD and KCD, claim state-of-the-art detection performance and better generalization to unseen attack types while reducing over-rejection, with code released on GitHub. Details →First reported aclanthology.org
Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis
An EMNLP 2024 paper, "Towards Understanding Jailbreak Attacks in LLMs: A Representation Space Analysis" by Yuping Lin et al., studies why some LLM jailbreak strategies succeed and others fail by analyzing how harmful and harmless prompts behave in the model's representation space. The work aims to identify intrinsic properties of successful jailbreak attacks. Details →First reported arxiv.org
Scalable Hierarchical Attention Transformers for Multi-Turn Jailbreak Detection in Long Conversations
Researchers from Zscaler present a hierarchical attention transformer for detecting multi-turn jailbreaks that spread unsafe intent across a conversation via gradual escalation, reframing, and role manipulation. The two-level architecture encodes individual turns with a multilingual encoder then reasons over turn summaries with a lightweight conversation module, reporting an F1 of 0.9394 on a 14,038-conversation benchmark and outperforming a strong LLM baseline while halving false positives. Details →First reported arxiv.org
Behind the Refusal: Determining Guardrail Activation via Behavioral Monitoring
Researchers from Mindgard and Lancaster University (William Hackett, Peter Garraghan) present the first black-box guardrail reconnaissance methodology, which detects whether a target AI system has a guardrail by monitoring HTTP, lexical, and timing signals during benign versus malicious prompt sets. The approach assumes zero prior knowledge and reportedly detects guardrail presence with 100% accuracy, letting adversaries distinguish a guardrail block from an LLM safety rejection to better select bypass techniques. Details →First reported github.com
GitHub - joshfischer1108/jailbreak-lab: A hands-on kit for educational, authorized red teaming of any locally-run LLM.
jailbreak-lab (a.k.a. LLM Red Team Lab) by joshfischer1108 is a hands-on GitHub kit for educational, authorized red teaming of any locally-run, OpenAI-compatible LLM (Llama, Mistral, Qwen, Gemma, DeepSeek R1, etc.). It bundles real jailbreak techniques including instruction override, roleplay/DAN, hypothetical framing, encoding, and payload splitting. Details →First reported github.com
GitHub - geno2009/Evil-Gemini-v1: Evil Gemini v1 - Jailbreaked Gemini 2.5 Flash
A GitHub repository named 'Evil-Gemini-v1' by user geno2009 is published as a jailbreak of Google's Gemini 2.5 Flash model, framed as an 'Evil Gemini' variant that removes the model's safety restrictions. The available page content shows only GitHub navigation boilerplate, with no detailed technical mechanism, README, or evaluation exposed. Details →First reported github.com
GitHub - Kirozaku/Marina-GPT: M.A.R.I.N.A GPT a powerful, evil brother of WormGPT.
Marina-GPT (M.A.R.I.N.A GPT), hosted on GitHub by user Kirozaku, is a publicly available Python tool marketed as an 'evil brother of WormGPT,' an uncensored/malicious LLM assistant. The repo contains runnable scripts (marina.py, cheyy-linux.py, requirements.txt), a 'Marina Pro' variant, and a Bitcoin wallet address for payment. Details →First reported github.com
GitHub - jailbreakwormGPT/wormGPT: wormGPT free version
A GitHub repository under the account 'jailbreakwormGPT' publishes what it calls 'WormGPT 4,' described as an unrestricted large language model designed for automating text generation, distributed with a worm.py script and framed as a free version of the known malicious WormGPT tool. Details →First reported github.com
GitHub - gaur-avvv/XGPT-WormGPT: [New Update](Added Agentic-Mode, Dark-GODMode)The Real BlackHat GPT - ai can do your illegal stuffs without saying anything. Use At Your Own Risk!
The GitHub repository XGPT-WormGPT (by user gaur-avvv) distributes a collection of jailbreak prompt files — including Agentic-Mode.txt, Dark-GODMode.txt, Dark.txt, and Gpt.txt — designed to bypass LLM safety guardrails and make AI models produce illicit content. Commit messages describe adding a 'fictional character WormGPT with malicious traits,' using the roleplay-persona framing as a technique to evade model refusals. Details →First reported github.com
GitHub - gaur-avvv/wormxgpt: No limits. No filters. No restrictions. WormXGPT is a unified AI tooling suite containing both a premium Hacker-themed React Web Dashboard and an advanced Unfiltered CLI agent. It features 150+ tools, multi-server MCP integration, auto-fallback across 30+ providers, and local workspace integration.
WormXGPT is a GitHub-published "unfiltered" AI tooling suite (repo gaur-avvv/wormxgpt) marketed with the tagline "No limits. No filters. No restrictions," combining a hacker-themed web dashboard and a CLI agent with 150+ tools, multi-server MCP integration, and auto-fallback across 30+ AI providers. The project is presented as an unrestricted, jailbroken AI agent framework echoing WormGPT-style malicious LLM tooling. 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 phishfort.com
Multimodal AI Jailbreak Attacks: The Image-Based Threat to Enterprise AI
Researchers at Florida International University (Hadi Amini and Md Jueal Mia) developed JaiLIP (Jailbreaking with Loss-guided Image Perturbation), a technique that embeds instructions into images via pixel-level perturbations invisible to humans but readable by vision-language models. Tested against BLIP-2, JaiLIP nearly doubled the rate of policy-violating outputs, and because most enterprise guardrails only inspect the text channel, the malicious payload in the image tensor bypasses safety filters entirely. 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 · updated · 2 reports hiddenlayer.com
EchoGram and guardrail bypass: are AI defenses keeping up?
HiddenLayer research dubbed EchoGram demonstrates that carefully chosen token sequences can flip verdicts in LLM guardrail models, causing harmful prompts to be marked safe or benign prompts to trigger false alarms. The NHIMG editorial summarizes the finding and its implications for organizations relying on probabilistic AI safety layers to protect deployed LLMs and agents. 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 github.com
LLM-Security-Jailbreak/docs/INTEGRITY_LEDGER.md at main · wherewolf87/LLM-Security-Jailbreak
The LLM-Security-Jailbreak repository, a fork of elder-plinius/T3MP3ST, publishes an 'Integrity Ledger' documenting a hardened jailbreak/security-testing harness run against the 104-challenge XBEN black-box benchmark across models including GLM-5.2 and gpt-5.5, with self-audited results tracking contamination and methodology corrections. Details →First reported arxiv.org
Mechanistic Interpretability of LLM Jailbreaks via Internal Attribution Graphs
Researchers from the University of South Dakota and Yangzhou University introduce a mechanistic interpretability framework that diagnoses LLM jailbreaks and adversarial prompts by constructing and aligning internal computation (attribution) graphs for clean versus attacked prompts. The method decomposes internal reasoning into invariant, suppressed, and emergent structures, identifies recurring vulnerability motifs, and performs causal interventions that improve robustness across multiple open-source LLMs and jailbreak benchmarks. Details →First reported giskard.ai
Prompt Injection | Giskard Documentation
Giskard's documentation describes the prompt-injection vulnerability category of its LLM red-teaming scanner, cataloguing runnable probes such as Best-of-N, DAN jailbreaks, math/Likert/citation/grandma framing, ASCII smuggling, encoding, transliteration, and the Deepset injection dataset used to test AI agents against OWASP LLM01. The probes reference underlying research including Best-of-N jailbreaking and Palo Alto Unit 42 multi-turn techniques. Details →First reported arxiv.org
Prompt Injection as Role Confusion
The paper "Prompt Injection as Role Confusion" (arXiv:2603.12277) argues that LLMs infer a text's role from stylistic cues rather than its labeled role tags, so untrusted text that imitates a trusted role inherits that role's authority. The authors introduce "role probes" to measure internal role perception and demonstrate CoT Forgery, a zero-shot attack that injects fabricated reasoning into user prompts and tool outputs, achieving ~60% attack success on frontier models against near-zero baselines. Details →First reported openai.com
GPT-5.5 Bio Bug Bounty
OpenAI announced its Bio Bounty Program (evolving from the GPT-5.5 Bio Bug Bounty), a private bounty inviting researchers to find universal jailbreaks that defeat the biosafety safeguards on frontier models GPT-5.5 and GPT-5.6. Rewards for a universal jailbreak were raised from $25,000 to $50,000, with smaller awards for partial wins. Details →First reported arxiv.org
Beyond Refusal: A Same-Lineage Study of Aligned and Abliterated LLMs for Vulnerability Analysis
A study titled 'Beyond Refusal' compares aligned instruction-tuned LLMs against their refusal-ablated (abliterated) descendants within the same Gemma and Qwen model lineages, measuring defensive utility across vulnerability detection, CWE attribution, line/root-cause localization, and patch validation. The authors find abliterated models achieve higher patch-validation and localization rates than aligned versions, and argue security-assistant evaluations should jointly measure response willingness, correctness, and actionability. Details →First reported huntress.com
New BioShocking attack manipulates AI browser into data theft
A new prompt injection attack dubbed 'BioShocking' reportedly tricks AI-powered browsers into treating risky real-world actions as part of a fictional scenario, bypassing safety guardrails and enabling data theft. Details →First reported · updated · 2 reports adversa.ai
GuardFall: a universal shell injection vulnerability in open-source AI agents
Adversa AI research describes GuardFall, a bypass of AI coding agents' safety filters using decades-old shell injection tricks to execute dangerous commands. The bypass reportedly worked against 10 of 11 tested popular open-source coding and computer-use agents, with only 'Continue' being resistant. Details →First reported thehackernews.com
New BioShocking Attack Tricks AI Browsers Into Leaking User Credentials
LayerX disclosed a technique called BioShocking that convinces AI browsers they are playing a game, tricking them into copying a user's credentials and sending them to an attacker. Six AI browsers and assistants were affected, including OpenAI's ChatGPT Atlas, Perplexity's Comet, and Anthropic's Claude browser extension. Details →First reported reliable-ai.review
On the Impossibility of Mitigating AI Jailbreaks – AI RELIABILITY REVIEW
A blog post (an intuitive version of the NeurIPS 2025 paper 'Mission Impossible: A Statistical Perspective on Jailbreaking LLMs') argues that alignment post-training only reshapes a model's output distribution without imposing hard constraints, making jailbreaks and prompt injections systematically exploitable. It illustrates this with reported real-world failures (McDonald's bot solving python puzzles, xAI chatbots giving bomb instructions, ChatGPT reproducing copyrighted characters). Details →First reported pypi.org
prompt-injection-defense
prompt-injection-defense is a Python package (pip install prompt-injection-defense) providing lightweight detection of prompt injection, jailbreaks, indirect injection, obfuscation, and unsafe content aligned to the OWASP LLM Top 10. It is an installable, MIT-licensed library maintained by Rajat Ghosh. Details →First reported adversa.ai
Solving the “Breaking the Prompt” DEF CON AI CTF with AI Red Teaming Agent
A write-up describing how Adversa AI's AI Red Teaming Agent solved a five-stage prompt injection/jailbreak CTF challenge ('Breaking the Prompt' by TrendAI at DEF CON), detailing which LLM jailbreak techniques worked and how they relate to guardrail design. Details →How the wire is made
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