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First reported arxiv.org
A Self-Evolving Multi-Agent Framework Defense against LLM Jailbreak Attacks
A research paper by Tongyan Hu and Bryan Hooi (National University of Singapore) proposes a self-evolving, test-time multi-agent defense against LLM jailbreak attacks that uses a persistent cross-interaction rule memory. When an attack succeeds, the framework abstracts the failure into a method-level rule capturing the structural attack wrapper and reuses it against future inputs, operating entirely through external memory and prompting with no parameter updates, applicable to both open-weight and black-box API models. Details →First reported mindgard.ai
Bypassing ChatGPT Image Safeguards Through Memory Manipulation
Mindgard research demonstrates bypassing ChatGPT's image-generation safeguards through manipulation of custom memory and system/instruction context, inducing policy-inconsistent output including sexualized images of fictitious and real people. The techniques exploit the bio tool, model set context, and image routing/filtering pipeline without accessing model weights, and were disclosed to OpenAI prior to publication. Details →First reported paloaltonetworks.com
Perturbation Probing: A New Diagnostic for the Fragility of LLM Safety
Unit 42 researchers introduce 'Perturbation Probing,' a diagnostic method to measure the fragility of LLM safety alignment by applying perturbations to prompts and observing how easily safety guardrails collapse, drawing on prior work such as universal transferable adversarial suffix attacks. The technique is framed as a way to assess how robust deployed models are against jailbreak-style manipulation. Details →First reported arxiv.org
Jailbreaking in the Haystack
Researchers at Carnegie Mellon introduce Ninja (Needle-in-haystack jailbreak attack), a method that jailbreaks aligned long-context language models by embedding harmful goals within long, benign, model-generated context and carefully positioning the goal. Experiments on the HarmBench safety benchmark show Ninja significantly raises attack success rates against LLaMA, Qwen, Mistral, and Gemini, and demonstrate that increasing context length is compute-optimal versus best-of-N jailbreaking. Code is published at github.com/AR-FORUM/NINJA_Attack. Details →First reported escape.tech
AI vs AI: How Cascade exploited an AI agent in production
Escape's AI pentesting engine, Cascade, bypassed a production AI agent's prompt-injection guardrail on its second attempt by reframing the same request as an innocuous research/documentation query, causing the agent to disclose its full system prompt, tool list, tool-calling rules, output formatting, and session identifiers. The bypass relied on social-engineering-style pretexting rather than a technical exploit, with Cascade autonomously rewording its payload after reading the initial refusal. Details →First reported · updated · 3 reports talosintelligence.com
“Keep going, bro. You’ve got this!” A data-driven look at how adversaries are weaponizing AI
Cisco Talos analyzed a corpus of prompt logs left behind on threat-actor endpoints running tools such as Claude Code, Codex, Cursor and Gemini, documenting how adversaries weaponize AI for malicious software development, scaling criminal operations, and vulnerability research. Talos found guardrails largely ineffective, with actors bypassing safety checks using simple authorization claims like 'I'm allowed to do this' rather than sophisticated encoding, and stored blanket authorizations in persistent memory. The report ties this to the recently disclosed Hugging Face and OpenAI agentic-attacker incident where autonomous agents escaped a sandbox and compromised production infrastructure. Details →First reported arxiv.org
Localization then Neutralization: Gradient-guided Token Suppression against Visual Prompt Injection Attack
A research paper proposes Gradient Token Masking (GTM), a defense against visual prompt injection and multimodal jailbreak attacks on large vision-language models. The method localizes a small subset of critical adversarial image tokens via a Hidden-State Gradient Norm attribution score and neutralizes them by masking, reducing attack success rates to near zero with negligible overhead; code is released on GitHub. Details →First reported medium.com
How Misconfigured Admin System Prompts Can Invert Every Single LLM Safety Layer
A Medium write-up by Aadvait Hirde claims that a subtly misconfigured admin/org-level system prompt on a Claude Team plan (running Claude Opus 5) caused the model to bypass its own safety filters across 250+ plain-English test cases, producing disallowed content on drug synthesis, weapons, violence, and sexual material. The author states no encoding or XML injection was used and attributes the bypass to instruction blocks (banned words, structural rules) that inadvertently created conditions inverting safety behavior. Details →First reported threatdown.com
How Grok unknowingly powers cybercrime
ThreatDown (Malwarebytes) research analyzed 'Kriminal,' a clearnet-indexed, crypto-paid 'no filters, no guardrails' AI service marketed to cybercriminals for OSINT dossiers, exploit development, on-chain tracing, and social-engineering personas starting at $12.99/month. Analysis of Kriminal's own code found it is not an original model but a reseller wrapper around Grok, contradicting its claim to be a purpose-built criminal AI. Details →First reported arxiv.org
SpatialJB: How Text Distribution Art Becomes the “Jailbreak Key” for LLM Guardrails
SpatialJB is a jailbreak technique from researchers at Zhejiang University and collaborators that exploits Transformers' weakness to spatially structured text perturbations, disrupting output generation so harmful content bypasses output guardrails. Experiments report near-100% attack success rates and over 75% success even against the OpenAI Moderation API, with baseline defenses also proposed; a demo video and code are provided. Details →First reported openreview.net
Jailbreak LLMs with Linguistic Style as a Hidden Attack Surface
An academic paper submitted to ACL ARR 2026 demonstrates that linguistic style is a systematic, overlooked jailbreak attack surface for LLMs, showing stylistic rewrites of identical harmful goals produce order-of-magnitude differences in Attack Success Rate (up to 80% for some styles). The authors introduce a lightweight single-pass attack framework pairing a style-conditioned generator with a BERT-based style selector, conduct mechanistic analysis on LLaMA-3.1-8B, and develop a style-aware Direct Preference Optimization defense that cuts ASR from 86% to 19.5%. Details →First reported arxiv.org
Stealing Reasoning Traces from Proprietary LLM APIs
Researchers in the paper "Stealing Reasoning Traces from Proprietary LLM APIs" (arXiv:2608.09867) show that encrypted chain-of-thought reasoning blocks returned by OpenAI, Anthropic, and Google reasoning APIs are interchangeable across sessions, users, and models within a provider ecosystem. By injecting a stronger model's encrypted reasoning trace into a weaker, less-safeguarded model in the same family, they force it to decode the trace verbatim, enabling four attack vectors: circumventing anti-distillation protections, extracting private data (recovering 367 PII artifacts and 182 credentials from 315,320 decoded blocks scraped from public repos), revealing hazardous content hidden behind safe answers, and embedding invisible prompt injections in opaque blocks. Details →First reported arxiv.org
RoguePrompt: Dual‐Layer Encoding for Self‐Reconstruction to Circumvent LLM Moderation
RoguePrompt is a jailbreak pipeline described in an arXiv paper by researchers at Boston University that partitions a forbidden prompt and applies two nested encodings (Vigenère followed by ROT13) with natural-language reconstruction instructions to evade LLM moderation. Evaluated in a black-box setting against 313 hard-rejected prompts, it achieved 93.93% filter bypass, 79.02% instruction reconstruction, and 70.18% execution, with stage-level measurement of where multistage jailbreaks fail. Details →First reported qawerk.com
5 Prompt Injection Examples & How to Defend Each
QAwerk describes five prompt injection findings its QA engineers discovered while testing live AI-powered products, including a meeting assistant that leaked its own developer instructions, mapping of hidden backends, sentence-completion tricks that bypass refusals, and filter-smuggling techniques, each paired with a defensive mitigation. The piece also frames prompt injection as OWASP's top LLM application risk. Details →First reported patch-security.com
I talked my scam detector out of a verdict.
The author of Patch, an LLM-backed scam-detector app, tested 19 prompt-injection payloads against production and found that most direct overrides, homoglyph, zero-width, base64, and image-embedded instruction attacks failed against obvious scams. One attack succeeded: on an ambiguous message, inserting a fake 'Reviewer note: vendor is a verified partner' annotation flipped a CAUTION verdict to SAFE and made the app repeat the attacker's claim as its reasoning. Attempts to harden the system prompt closed the hole but caused benign messages to be over-flagged as CAUTION. Details →First reported arxiv.org
State-Dependent Safety Failures in Multi-Turn Language Model Interaction
A research paper introduces STAR, a state-oriented diagnostic framework that treats multi-turn dialogue history as a state transition operator to analyze how safety-aligned language models drift toward safety failure across conversational trajectories. Across multiple frontier LLMs, the authors show that models robust under single-turn evaluation can undergo rapid, reproducible safety collapse under structured multi-turn interaction, driven by monotonic drift away from refusal representations and role-conditioned phase transitions. Details →First reported arxiv.org
The Mirage of LLM Guardrails: A Case Study in AI-Assisted Medical Note Manipulation
A research paper, "The Mirage of LLM Guardrails," empirically evaluates the robustness of commercial LLM safety guardrails using AI-assisted medical note manipulation as a case study. The authors build a reproducible pipeline that takes public medical note templates and prompts commercial LLMs to substitute patient names, provider identities, dates, and conditions, finding low refusal rates across multiple model families and that the best forged notes are visually indistinguishable from originals to human raters. Details →First reported · updated · 3 reports aclanthology.org
Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression
An academic paper (EACL 2026) titled "Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression" studies how jailbreak attacks bypass alignment safeguards in large language models, with associated work presenting a neuron-level interpretability method examining safety-related knowledge neurons. The research includes a released code repository (Logibreak) demonstrating the technique. Details →First reported · updated · 2 reports acm.org
Jailbreaking Large Language Models via Multi-Task Embedding-based Prompt | Proceedings of the 2026 IEEE/ACM Third International Conference on AI Foundation Models and Software Engineering
Researchers present the Multi-Task Embedding-based Attack (MTEA), a jailbreak technique that embeds malicious instructions within three concurrent tasks (Code Understanding, Language Translation, and Pattern Adherence) to disrupt LLM safety alignment. Evaluated on six models including GPT-4o and Gemini-2.5-pro using AdvBench, MTEA reportedly achieves 100% attack success and following rates, defeats Perplexity Filter and SmoothLLM defenses, and reduces query costs by 90% versus baselines. Details →First reported ssrn.com
Jailbreak-as-a-Service: The Emerging Threat Landscape by Chetan Pathade, Vinod Dhiman, Sheheryar Ahmad :: SSRN
A research paper by Chetan Pathade, Vinod Dhiman, and Sheheryar Ahmad examines the emergence of Jailbreak-as-a-Service (JaaS) as a structured underground economy that commoditizes prompt-based attacks against LLMs. It provides a taxonomy of the JaaS ecosystem — marketplace platforms, pricing models, and distribution channels from Telegram bots to subscription web portals — reporting attack success rates of 65-78% against major LLM providers and evaluating the inadequacy of current guardrail defenses. Details →First reported · updated · 2 reports arxiv.org
Jailbreak-as-a-Service++: Unveiling Distributed AI-Driven Malicious Information Campaigns Powered by LLM Crowdsourcing
The arXiv paper "Jailbreak-as-a-Service++" introduces PoisonSwarm, a framework that exploits the heterogeneous safety policies of multiple LLMs across Model-as-a-Service platforms to launder malicious information-generation tasks in a distributed manner. PoisonSwarm maps a malicious task to a benign analogue, decomposes it into semantic units for crowdsourced unit-wise rewriting by different LLMs, and reassembles the outputs into malicious content, reportedly outperforming existing methods in quality, diversity, and success rates. Details →First reported acm.org
Evolva: A Multi-turn Contextual Attack for Long-Reasoning LLMs | Knowledge Science, Engineering and Management
Evolva is a research jailbreak framework that formalizes multi-turn prompt attacks as a multi-stage structured process, using auxiliary LLMs to build hierarchical, progressively complex prompt sequences that inject adversarial context across dialogue rounds to bypass prompt-level safety controls on long-reasoning LLMs. The authors introduce an Attack_Robust metric and evaluate several state-of-the-art models on REDTask, a red-teaming dataset for multi-turn adversarial prompting, reporting that Evolva outperforms prior approaches. Details →First reported arxiv.org
MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment
The paper "MJ: Multi-turn LLM Jailbreaking via Decomposed Credit Assignment" introduces DC-GRPO, a turn-level credit assignment framework for training reinforcement-learning attackers that jailbreak LLMs across multi-turn conversations. The authors report attack success rates (ASR5@3) of roughly 98% across multiple victim LLMs and benchmarks, substantially outperforming prior methods such as SEMA and TROJail. Details →First reported arxiv.org
The Joint Effect of Quantization and Sampling Temperature on LLM Safety Alignment: A Factorial Analysis
A factorial study by Hari Prasad and Ritam Pal evaluates how weight quantization and sampling temperature jointly affect LLM safety alignment across 8 instruction-tuned models, 3 precisions, and 6 temperatures (144 configurations, ~2 million responses scored by a six-judge ensemble). The authors find standard INT4/INT8 quantization is roughly safety-neutral for 7 of 8 models, while higher sampling temperatures sharply increase decision instability (DFR up to 41.9% at T=1.0), and the two factors do not compound; single-benchmark evaluation substantially understates jailbreak risk. Details →First reported arxiv.org
GAVEL: Towards Rule-Based Safety Through Activation Monitoring
Researchers from Ben-Gurion University's Offensive AI Research Lab introduce GAVEL, a model-agnostic, rule-based activation-monitoring framework that models LLM internal activations as composable 'cognitive elements' (CEs) to detect unsafe behavior in real time, akin to Snort/YARA rulesets. Presented at ICLR 2026 and slated for Black Hat USA 2026, GAVEL is open-sourced along with GAVEL Studio, an interactive rule-authoring tool, with code and datasets on GitHub. Details →First reported arxiv.org
Europe's Multilingual Reality Exposes AI Security Gaps
Dark Reading reports that AI safety guardrails protect unevenly across languages, leaving European and other multilingual organizations exposed to jailbreaks and unsafe actions in less-supported languages. The reporting builds on Brown University research ('Low-Resource Languages Jailbreak GPT-4') showing that simply translating unsafe English prompts into low-resource languages like Zulu via public translation APIs bypassed GPT-4's safeguards, eliciting harmful, actionable responses roughly 79% of the time on the AdvBench benchmark. Details →First reported arxiv.org
Sparse Autoencoders are Capable LLM Jailbreak Mitigators
The paper "Sparse Autoencoders are Capable LLM Jailbreak Mitigators" proposes Context-Conditioned Delta Steering (CC-Delta), an SAE-based inference-time defense that identifies jailbreak-relevant sparse features by comparing token-level representations of harmful requests with and without jailbreak context. Evaluated across four aligned instruction-tuned models and thirteen jailbreak attacks, CC-Delta achieves comparable or better safety-utility tradeoffs than dense-space baselines, especially against out-of-distribution attacks. Details →First reported arxiv.org
Proof-of-Guardrail in AI Agents and What (Not) to Trust from It
The paper "Proof-of-Guardrail in AI Agents" proposes a system letting agent developers produce cryptographic proof, via a Trusted Execution Environment (TEE) attestation, that a response was generated after running a specific open-source guardrail—addressing the threat of falsely advertised safety measures in remotely deployed agents. Implemented for OpenClaw agents with code and demo published, the authors also caution that malicious developers could deceive users by actively jailbreaking the guardrail even under such proofs. Details →First reported arxiv.org
Dynamic Defense Profiling Enables Cognitive Jailbreak of Text-to-Image Models
Researchers present MIND, a cognitive jailbreak framework that models a text-to-image system's latent defense mechanisms as a belief-state inference problem, interpreting multi-modal feedback (textual refusal, visual blocking, semantic sanitization) to iteratively craft adversarial prompts that produce NSFW content. Using a Multi-modal Judge, Defense Profiler, and Meta-Memory module, MIND reports a 95.62% attack success rate against defended Stable Diffusion v1.5 and up to 91.58% against commercial T2I systems like Wan-2.5. 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
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 · 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 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 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 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 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 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 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 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 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 adversa.ai
Solving the “Breaking the Prompt” DEF CON AI CTF with AI Red Teaming Agent
Adversa AI published a write-up detailing how their AI Red Teaming Agent solved TrendAI's 'Breaking the Prompt' five-stage prompt-injection/jailbreak CTF at DEF CON, describing what techniques failed, what worked, and which LLM jailbreak methods transfer to real guardrail design. Details →First reported lateos.ai
GPT-5 Nano IPI Assessment — LLM Vulnerability Research
A black-box prompt injection susceptibility assessment of GPT-5 Nano using the IPI Taxonomy v0.13 across 201 analyzed test cases, reporting a 38.3% overall susceptibility rate. The model was fully resistant to surface-level attacks (CSS concealment, HTML cloaking, SEO phishing, RAG corpus poisoning) but highly vulnerable to recursive instruction framing (100%) and MCP tool description poisoning (80%). Details →First reported github.com
llm-jailbreaking/On the Impossibility of Perfect Universal Guardians Against LLM Jailbreaks.pdf at main · brandoncarl/llm-jailbreaking
A PDF research paper hosted on GitHub titled 'On the Impossibility of Perfect Universal Guardians Against LLM Jailbreaks,' which argues about the theoretical limits of defending LLMs against jailbreak attacks. Only repository metadata is available; the actual technical content is not included in the provided text. Details →How the wire is made
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