AI agents face new prompt injection and backdoor attacks
ByPulseAugur Editorial·[104 sources]·
Researchers are developing new methods to attack and defend AI agents used in software reverse engineering and cybersecurity. One approach uses genetic algorithms to inject malicious prompts into AI agents, causing them to misinterpret code and bypass detection systems. Other studies focus on detecting and obfuscating these prompt injection attacks, as well as defending against multi-step trojan attacks that embed persistent control within agent workflows. Additionally, a framework called CVE-Factory automates the creation of executable vulnerability tasks for training and evaluating code security agents, showing significant improvements in models like Qwen3-32B.
AI
IMPACT
New attack vectors and defense mechanisms for AI agents highlight critical security vulnerabilities in AI-powered tools.
RANK_REASON
Multiple research papers detailing novel attacks and defenses against AI agents in security contexts.
arXiv:2606.11632v1 Announce Type: cross Abstract: Agentic infrastructure introduces a critical control-plane authorization problem: non-deterministic reasoning systems can propose high-stakes mutations to production resources, yet existing security mechanisms -- such as identity …
Agentic infrastructure introduces a critical control-plane authorization problem: non-deterministic reasoning systems can propose high-stakes mutations to production resources, yet existing security mechanisms -- such as identity and access management (IAM), policy engines, conse…
arXiv:2606.07150v1 Announce Type: cross Abstract: Agent-interoperability protocols such as A2A and MCP standardize what agents say to one another, but assume address-based transport over HTTP(S). Such transports protect message content, increasingly with end-to-end encryption. Wh…
arXiv:2606.06460v1 Announce Type: cross Abstract: As autonomous LLM agents increasingly hold real credentials and operate infrastructure without a human in the loop, operators have no standard way to tell an agent that a resource is off-limits. Access controls either let the agen…
arXiv cs.AI
TIER_1English(EN)·Hanna Foerster, Tom Blanchard, Kristina Nikoli\'c, Ilia Shumailov, Cheng Zhang, Robert Mullins, Nicolas Papernot, Florian Tram\`er, Yiren Zhao·
arXiv:2601.09923v3 Announce Type: replace Abstract: AI agents are vulnerable to prompt injection attacks, where malicious content hijacks agent behavior. Among proposed defenses, architectural isolation provides the strongest guarantees by strictly separating trusted task plannin…
arXiv:2606.05679v1 Announce Type: cross Abstract: Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users. While recent work improves query correctness, correctness is not safety. A query may be semantically valid yet violate regulat…
arXiv cs.AI
TIER_1English(EN)·Rufat Asadli, Benjamin Hoffman, Ioannis Protogeros, Laurent Vanbever·
arXiv:2606.06212v1 Announce Type: new Abstract: Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automate the complex, error-prone task of network configuration. However, even state-o…
Agent-interoperability protocols such as A2A and MCP standardize what agents say to one another, but assume address-based transport over HTTP(S). Such transports protect message content, increasingly with end-to-end encryption. What they leave in the clear is the communication gr…
arXiv:2606.05233v1 Announce Type: cross Abstract: Recent computer-using-agent (CUA) red-teaming papers report prompt-injection attack success rates (ASR) of 42-98%, but these headline numbers cluster on retired models and on the most-vulnerable model in each paper's panel. We ask…
As autonomous LLM agents increasingly hold real credentials and operate infrastructure without a human in the loop, operators have no standard way to tell an agent that a resource is off-limits. Access controls either let the agent in (it has valid credentials) or hard-fail it (i…
Misconfigurations in computer networks remain a major source of critical Internet outages. Research is turning to Large Language Models (LLMs) to automate the complex, error-prone task of network configuration. However, even state-of-the-art models fail to resolve misconfiguratio…
Agents increasingly generate SQL, orchestrate pipelines, and automate data analysis on behalf of users. While recent work improves query correctness, correctness is not safety. A query may be semantically valid yet violate regulatory, privacy, or business constraints that govern …
arXiv:2606.04329v1 Announce Type: cross Abstract: Memory is a core component of AI agents, enabling them to accumulate knowledge across interactions and improve performance. However, persistent memory introduces the risk of memory poisoning, where a single adversarial memory writ…
arXiv:2603.03205v2 Announce Type: replace Abstract: Agentic language models operate in a fundamentally different safety regime than chat models: they must plan, call tools, and execute long-horizon actions where a single misstep, such as accessing files or entering credentials, c…
arXiv:2606.04990v1 Announce Type: cross Abstract: Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents. These capabilities expand agent autonomy, but also mak…
arXiv cs.AI
TIER_1English(EN)·Tianneng Shi, Robin Rheem, Dongwei Jiang, Mona Wang, Francisco De La Riega, Zhun Wang, Jingzhi Jiang, Alexander Cheung, Sean Tai, Jonah Cha, Jianhong Tu, Gabriel Han, Chenguang Wang, Jingxuan He, Wenbo Guo, Dawn Song·
arXiv:2606.04460v1 Announce Type: cross Abstract: AI has the potential to transform cybersecurity by enabling systems that can autonomously detect, analyze, and remediate software vulnerabilities. However, existing cybersecurity evaluations of AI systems are limited in scale or s…
arXiv cs.AI
TIER_1English(EN)·Yuanbo Xie, Tianyun Liu, Yingjie Zhang, Suchen Liu, Yulin Li, Liya Su, Tingwen Liu·
arXiv:2606.04425v1 Announce Type: cross Abstract: Modern agentic systems transform LLMs from session-bounded assistants into stateful systems that persist and evolve shared world state across sessions through memories, filesystems, tools, and other long-lived contextual artifacts…
arXiv:2606.04193v1 Announce Type: cross Abstract: Current AI agent observability is structurally compromised: the entity producing the activity log is the same entity whose activity is being logged. A compromised or buggy agent can omit, alter, or fabricate its own traces, and th…
Large language model (LLM)-based agents increasingly solve complex tasks by interacting with external tools, retrieval systems, memory modules, environments, and other agents. These capabilities expand agent autonomy, but also make agent behavior harder to verify, debug, and audi…
arXiv:2606.03163v1 Announce Type: cross Abstract: This paper describes the technical architecture of OpenAgenet / OAN. OAN is a protocol-neutral trust layer for open Agent interconnection. It specifies the role architecture, identity objects, registration workflow, Root-governed …
arXiv cs.AI
TIER_1English(EN)·Eliot Krzysztof Jones, Mateusz Dziemian, Matt Fredrikson, J Zico Kolter·
arXiv:2606.02644v1 Announce Type: cross Abstract: Agentic scaffolds have dramatically improved LLM performance on complex, long-horizon tasks, yielding both broad benefits and amplified risks in domains like cybersecurity. Existing benchmarks for AI agents in cybersecurity focus …
arXiv:2606.03161v1 Announce Type: cross Abstract: OpenAgenet, abbreviated as OAN, is an open infrastructure project for trusted Agent interconnection. It addresses a problem that becomes visible when Agents move from isolated applications into open, multi-operator networks: befor…
This paper describes the technical architecture of OpenAgenet / OAN. OAN is a protocol-neutral trust layer for open Agent interconnection. It specifies the role architecture, identity objects, registration workflow, Root-governed lifecycle, Root-verified package model, authorizat…
This paper describes the technical architecture of OpenAgenet / OAN. OAN is a protocol-neutral trust layer for open Agent interconnection. It specifies the role architecture, identity objects, registration workflow, Root-governed lifecycle, Root-verified package model, authorizat…
OpenAgenet, abbreviated as OAN, is an open infrastructure project for trusted Agent interconnection. It addresses a problem that becomes visible when Agents move from isolated applications into open, multi-operator networks: before an Agent can safely discover, select, and invoke…
OpenAgenet, abbreviated as OAN, is an open infrastructure project for trusted Agent interconnection. It addresses a problem that becomes visible when Agents move from isolated applications into open, multi-operator networks: before an Agent can safely discover, select, and invoke…
arXiv:2605.17909v2 Announce Type: replace Abstract: As autonomous agentic systems scale across regulated critical infrastructures, the lack of mechanistic, hardware-rooted enforcement for high-frequency policy updates presents a fundamental safety gap. We present Ethical Hyper-Ve…
arXiv cs.AI
TIER_1English(EN)·Ismail Hossain, Sai Puppala, Zhuoran Lu, Sajedul Talukder, Nan Jiang·
arXiv:2606.00925v1 Announce Type: cross Abstract: Open agent platforms allow community contributors to publish reusable skills that agents can invoke at runtime. This extensibility also creates a supply-chain risk: malicious contributors can hide harmful behavior inside skills th…
arXiv cs.AI
TIER_1English(EN)·Florian Holzbauer, David Schmidt, Gabriel Gegenhuber, Sebastian Schrittwieser, Johanna Ullrich·
arXiv:2603.16572v2 Announce Type: replace-cross Abstract: Agent skills extend local AI agents, such as Claude Code and OpenClaw, with additional functionality. Their growing popularity has led to dedicated marketplaces resembling mobile app stores, as well as automated scanners t…
arXiv:2602.06547v3 Announce Type: replace-cross Abstract: LLM-based coding agents increasingly rely on third-party extensions called skills, which bundle natural language instructions and helper scripts that execute with full user privileges. Community registries have emerged to …
arXiv:2606.00341v1 Announce Type: cross Abstract: As AI agents are increasingly deployed in real personal and corporate settings (email accounts, development workflows, company databases, etc.), safety considerations surrounding these agents become paramount. Although much work h…
arXiv:2606.00497v1 Announce Type: cross Abstract: Deceptive web content, widely instantiated across the internet and commonly known as \textit{social-engineering attacks}, manipulates autonomous web agents into submitting users' personally identifiable information (PII) to attack…
arXiv:2606.01166v1 Announce Type: cross Abstract: Computer-use agents extend language models from text generation to sustained interaction with files, terminals, browsers, and external tools. This shift creates safety risks that are difficult to detect from isolated prompts or fi…
arXiv:2606.01567v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly rely on reusable skills i.e. documents describing task-specific procedures. However, this introduces a new attack surface for agents to manage. We study two complementary directions f…
arXiv:2606.02302v1 Announce Type: cross Abstract: Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services. While such capabilities enable complex real-world workflows, they also introduce security risks tha…
BraveGuard is a self-evolving defense framework that trains guard models using open-world threat signals and realistic agent trajectories to improve safety detection in computer-use agents.
Autonomous LLM agents increasingly operate in stateful environments where they access tools, files, memory, and external services. While such capabilities enable complex real-world workflows, they also introduce security risks that are difficult to capture with existing evaluatio…
arXiv cs.AI
TIER_1English(EN)·Brian Crawford, Patrick McClure·
arXiv:2605.30677v1 Announce Type: cross Abstract: Agentic software reverse engineering systems are vulnerable to prompt injection attacks placed into the source code of executable binary files. This research demonstrates defensive tactics for detecting the presences of prompt inj…
arXiv:2602.03012v3 Announce Type: replace-cross Abstract: Evaluating and improving the security capabilities of code agents requires high-quality, executable vulnerability tasks. However, existing works rely on costly, unscalable manual reproduction and suffer from outdated data …
arXiv cs.AI
TIER_1English(EN)·Brian Crawford, Justin Phillips, Patrick McClure·
arXiv:2605.30667v1 Announce Type: cross Abstract: Software tools for reverse engineering executable binary files, such as Ghidra, enable malware analysts to safely conduct robust static analysis without having access to original source code. Coupled with the analytic power of lar…
arXiv:2605.31042v1 Announce Type: cross Abstract: LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and write files, call tools, and reuse workspace state across sessions. While such cap…
LLM agents are evolving from conversational chatbots to operational tools in real-world workspaces. In local agentic harnesses, an LLM can read and write files, call tools, and reuse workspace state across sessions. While such capabilities enhance utility, they also expose a new …
arXiv:2605.28914v1 Announce Type: cross Abstract: Tool-using language agents turn model decisions into external side effects: they read files, run scripts, call APIs, send messages, and invoke Model Context Protocol tools. This makes agent attacks different from jailbreaks. The h…
arXiv:2605.30096v1 Announce Type: cross Abstract: Large language models (LLMs) can autonomously conduct multi-stage cyber attacks, but the consistency of their offensive behavior under repeated trials remains unstudied. This work presents the first large-scale empirical measureme…
arXiv cs.AI
TIER_1Svenska(SV)·Yunhao Feng, Yifan Ding, Yingshui Tan, Boren Zheng, Yanming Guo, Xiaolong Li, Kun Zhai, Yishan Li, Wenke Huang·
arXiv:2604.06811v2 Announce Type: replace-cross Abstract: Skill-based agent systems tackle complex tasks by composing reusable skills, improving modularity and scalability while introducing a largely unexamined security attack surface. We propose SkillTrojan, a backdoor attack th…
Multi-step trojan attacks in local LLM agents can bypass existing defenses by embedding malicious prompts across multiple operations, requiring new detection methods like DASGuard for effective protection.
Large language models (LLMs) can autonomously conduct multi-stage cyber attacks, but the consistency of their offensive behavior under repeated trials remains unstudied. This work presents the first large-scale empirical measurement of LLM attack consistency: 400 autonomous penet…
Modern open-world agents such as OpenClaw exhibit powerful cross-environment execution capabilities yet introduce broad new safety risk sources. Meanwhile, advanced frontier AI models drastically lower attack barriers, rendering current agent alignment frameworks inadequate for r…
arXiv:2605.12015v2 Announce Type: replace-cross Abstract: Reusable skills are becoming a common interface for extending large language model agents, packaging procedural guidance with access to files, tools, memory, and execution environments. However, this modularity introduces …
arXiv:2605.27689v1 Announce Type: new Abstract: When machine learning systems under-perform for particular subgroups, affected users typically have no way to correct these disparities without relying on platform-level fixes. Existing approaches to algorithmic fairness rely on pro…
arXiv cs.AI
TIER_1English(EN)·Yaoyu Zhao, Yichen Xu, Oliver Bra\v{c}evac, Cao Nguyen Pham, Frank Zhengqing Wu, Martin Odersky·
arXiv:2605.28617v1 Announce Type: new Abstract: LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime owns the loop, context, and control flow, and the model has little say over any o…
arXiv:2605.27690v1 Announce Type: new Abstract: LLM agents increasingly operate through multi-turn tool use and environment interaction, where safety risks often emerge from intermediate steps long before they surface in the final outcome. Reactive auditing is therefore insuffici…
A lightweight and scalable agent safety alignment framework is proposed to address emerging threats from advanced AI models, featuring taxonomy-guided training with minimal samples and efficient deployment in real-world scenarios.
LLM agents increasingly act by writing code, yet a split persists between the runtime that drives the agent and the code the model writes. The runtime owns the loop, context, and control flow, and the model has little say over any of them. Letting model-written code shape the run…
arXiv:2505.11063v3 Announce Type: replace Abstract: LLM-based agents solve complex tasks through iterative reasoning, tool use, and environment interaction, where each intermediate thought directly shapes subsequent actions. Small deviations in these thoughts can therefore propag…
arXiv cs.AI
TIER_1English(EN)·Yige Li, Yunhao Feng, Jun Sun·
arXiv:2605.27117v1 Announce Type: new Abstract: AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of modern language models, but aligned behavior does not…
LACUNA is a programming model that enables LLM agents to write code that shapes the runtime while maintaining safety through type checking and controlled execution.
AI safety is still largely framed as alignment: training models to follow human preferences, safety policies, and normative constraints. That framing has improved the behavior of modern language models, but aligned behavior does not by itself guarantee that a deployed agent can b…
arXiv:2605.25707v1 Announce Type: new Abstract: Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-world execution environments are far from ideal: pop-…
arXiv:2605.23989v1 Announce Type: new Abstract: Agentic AI systems -- Large Language Models (LLMs) augmented with planning, tool use, memory, and long-horizon interactions -- can execute complex tasks autonomously, but their multi-step trajectories introduce new failure modes tha…
Autonomous computer use agents that powered by multimodal large language models (MLLMs) are emerging as capable assistants for completing complex digital workflows. However, real-world execution environments are far from ideal: pop-ups, resolution changes, and competing applicati…
arXiv:2602.12316v2 Announce Type: replace Abstract: Frontier AI systems are increasingly capable and deployed in high-stakes multi-agent environments. However, existing AI safety benchmarks largely evaluate single agents, leaving multi-agent risks such as coordination failure and…
arXiv:2602.04431v2 Announce Type: replace Abstract: LLM-based multi-agent systems have demonstrated impressive capabilities, but they also introduce significant safety risks when individual agents fail or behave adversarially. In this work, we study the automated design of agenti…
Computer-use agents powered by multimodal large language models face significant challenges in real-world environments due to dynamic disruptions, necessitating robustness evaluation and improved framework designs.
arXiv cs.CL
TIER_1English(EN)·Piercosma Bisconti, Matteo Prandi, Federico Pierucci, Federico Sartore, Enrico Panai, Laura Caroli, Yue Zhu, Adam Leon Smith, Luca Nannini, Marcello Galisai, Susanna Cifani, Francesco Giarrusso, Marcantonio Bracale Syrnikov, Daniele Nardi·
arXiv:2605.22643v1 Announce Type: new Abstract: Background. Traditional safety benchmarks for language models evaluate generated text: whether a model outputs toxic language, reproduces bias, or follows harmful instructions. When models are deployed as agents, the safety-relevant…
Background. Traditional safety benchmarks for language models evaluate generated text: whether a model outputs toxic language, reproduces bias, or follows harmful instructions. When models are deployed as agents, the safety-relevant object shifts from what the system says to what…
Background. Traditional safety benchmarks for language models evaluate generated text: whether a model outputs toxic language, reproduces bias, or follows harmful instructions. When models are deployed as agents, the safety-relevant object shifts from what the system says to what…
The benchmarks used to evaluate AI agents in security-critical roles suffer from crucial weaknesses. Building on recent empirical evidence, we characterize three core challenges that undermine security evaluations: benchmark vulnerabilities, temporal staleness, and runtime uncert…
METR (Model Evaluation & Threat Research)
TIER_1中文(ZH)·
<p style="text-align: center;"><a class="button button-primary button-wide max-width-100" href="https://metr.org/frontier-ai-regulations.pdf">Ver como PDF</a></p> <p>Los desarrolladores de IA de frontera como OpenAI, Google, Anthropic, xAI y otros tienen obligaciones de seguridad…
<p><span>Good type hints lead to code that is more </span><a href="https://link.springer.com/article/10.1007/s10664-013-9289-1" rel="noopener nofollow" target="_blank"><span>maintainable, easier to understand</span></a><span>, and with </span><a href="https://blog.acolyer.org/201…
AWS Machine Learning Blog
TIER_1English(EN)·Bharathi Srinivasan·
In this post, we use a lakehouse data agent to demonstrate how you can use Policy for deterministic access control and Lambda interceptors for dynamic validation. We then show how to combine Lambda interceptors and Policy to implement a geography-based access control which requir…
Without the right controls, consumer-facing AI agents can expose organizations to regulatory violations, privacy breaches, eroded trust and reputational damage.
Organizations are confronting the growing gap between AI hype and measurable business impact. This is exposing major blind spots in governance, usage visibility and operational oversight.
<p>In this tutorial, we build a governed AI-agent workflow using Microsoft’s Agent Governance Toolkit as the reference point. We create a Colab-ready implementation where agents do not directly execute tools; instead, every action first passes through a governance layer that chec…
dev.to — Claude Code tag
TIER_1English(EN)·Marcus Rowe·
<p>Your AI coding agent just became an attack vector.</p> <p>That's the short version of what Adversa AI published this week. The research team disclosed a technique called SymJack — a symlink hijacking attack that turns AI coding assistants into supply chain attack delivery syst…
dev.to — MCP tag
TIER_1English(EN)·Manveer Chawla·
<p>AI agents are now in production across healthcare, financial services, and critical SaaS systems. They mutate data, trigger workflows, and call external APIs on behalf of real users. These are autonomous actors, not the read-only recommendation engines that security teams alre…
<p>When an AI agent settles a trade with no human watching, something has to make that trade trustworthy. There are two serious ways to do it, and they are not the same. One puts a judge in the loop. The other replaces the judge with math. Most of the current debate about "trust …
<p>AI agent frameworks are bringing checkpoint/restore, time travel, and rewind into everyday developer workflows. If an agent makes a mistake, it can go back to a checkpoint. If a user wants to explore another path, the agent can branch from an earlier state. This is useful for …
<p>"Atomic" is having a moment. It is showing up in funding announcements, in launch threads, in agent-commerce pitch decks. This week a team raised <strong>$25M</strong> for an atomic OTC desk built on HTLCs and Bitcoin Taproot, with no custodian holding the assets mid-trade. Th…
<h1> 클로드를 협박에 쓰지 못하게 막는 것과, 클로드가 스스로 협박하지 않도록 만드는 것은 전혀 다른 문제다 </h1> <p><em>앤트로픽이 '클로드'의 자기검열을 설계한 방식 — 그리고 왜 이것이 단순한 필터 이야기가 아닌가</em></p> <p><a class="article-body-image-wrapper" href="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cform…
Medium — AI coding tag
TIER_1English(EN)·Anna Jey·
<h2> An Agent's Attack Surface Is Bigger Than You Think </h2> <p>A plain LLM application has one attack surface: user input → LLM output.</p> <p>Add tools to the mix, and it triples:<br /> </p> <div class="highlight js-code-highlight"> <pre class="highlight plaintext"><code>User …
"No Attack Required: Semantic Fuzzing for Specification Violations in Agent Skills" LLM-powered agents can silently delete documents, leak credentials, or transfer funds on a routine user request, not because the agent was attacked, but because the skill it invoked broke its own …
<p>Last week, I ran a simple experiment: I poisoned my own AI agent's memory with 3 lines of code. The result? The agent started leaking user data to an attacker-controlled endpoint — and it had no idea.</p> <h2> The Attack </h2> <p>Here's what memory poisoning looks like in prac…
<p>Autonomous AI agents are moving from research labs into production environments at speed. Unlike chatbots that respond to single prompts, agents plan, reason, execute multi-step tasks, call external tools, and delegate sub-tasks to child agents. With each of these capabilities…
<p>AI agents now send email, post messages, and call tools on their own. We spend a<br /> lot of energy guarding the <strong>input</strong> — the user's prompt. We spend almost none on<br /> the <strong>output</strong>: what the agent is actually about to send.</p> <p>That's the …
<!-- SC_OFF --><div class="md"><p>Hey everyone,</p> <p>We’re building <strong>Antitech</strong>, a security layer for AI agents and LLM-powered workflows.</p> <p>We’re opening a small number of free early-access assessments for teams/builders working on AI agents.</p> <p>If you g…
The Calculator Discipline — A Taxonomy and Pre-Send Filter for AI-Assisted Vulnerability Disclosure Hallucinations - Paper and Tool by Stuart Thomas, independent Security Researcher # Infosec # LLM # AI https:// stuart-thomas.com/research/cal culator-discipline/
<h2> The Silent Threat: When Your AI Turns Against You </h2> <p>Your AI agent is sorting through a thousand new customer support emails, summarizing key issues and drafting responses. It has access to your company's private knowledge base, customer data, and internal APIs. It’s a…
<p><strong>What:</strong> The <strong>Boiling the Frog</strong> benchmark is a stateful multi-turn safety eval for tool-using AI agents — it walks a scenario from benign edits to risk-bearing actions and scores whether the agent accepts the escalated final turn.</p> <p><strong>Wh…
dev.to — LLM tag
TIER_1English(EN)·Vaishnavi Gudur·
<p>The AI safety community has a blind spot. We have excellent benchmarks for measuring whether an LLM will output harmful content (like toxicity or jailbreaks), and we have benchmarks for measuring whether an agent can successfully complete a task (like SWE-bench or WebArena).</…
"Claw Patrol" : un firewall open-source conçu spécifiquement pour les agents IA. L'idée de base est solide — les agents autonomes ont une surface d'attaque différente des apps classiques : appels d'outils, chaînes de prompts, accès externes. Avoir une couche de contrôle dédiée, c…
<!-- SC_OFF --><div class="md"><p>Author here. The short version of why I built this:</p> <p>Cyber-AI evaluation is converging on the same diagnosis from multiple labs. Anthropic's Claude Mythos system card this year: their cyber ranges "lack many features often present in r…
<!-- SC_OFF --><div class="md"><p>I’ve been using and studying AI coding agents more, and the part I keep getting stuck on is not whether they can write code. They obviously can. The harder question is where trust is supposed to enter the workflow. If an agent touches files outsi…
