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3 stories in the last 7d

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LLMs are real, AI is fake

Cory Doctorow argues the OpenAI chatbot 'hacking' of Hugging Face was a Python-scripted CTF loop, not autonomous AI.

In an opinion essay, Cory Doctorow debunks reports that OpenAI chatbots autonomously hacked Hugging Face servers during an 'Exploit Gym' capture-the-flag challenge. He explains the chatbot merely acts as a front-end queried by a Python program that replays commands drawn from CTF training data. He argues sensational 'AI went rogue' narratives are amplified by technical press and help AI companies raise investment capital.

Hacker News · AI · 4d agoAI safety & securityHN 40↑ · 10 comments1

AgentLSD: Evaluating AI Security Agents Under Adversarial Task Contamination

AgentLSD benchmark shows deceptive CTF artifacts like fake flags and decoy endpoints steer AI security agents wrong, inflating turns and tokens.

The paper defines adversarial task contamination, where deceptive artifacts in agent environments, including non-instructional evidence beyond prompt injection, influence AI security agents. AgentLSD injects trap artifacts such as fake flags, misleading hints, decoy endpoints, and hidden cues into 11 web CTF challenges, evaluating six models with paired clean and trap-augmented runs. Clean-condition agents capture 41% of flags, and even successful captures see roughly +20 turns and +2k reasoning tokens, with heterogeneous solve-rate effects. The framework, configurations, and traces are released.

arXiv cs.CR · 17h agoAI safety & security

Evaluating Context Segmentation in Locally Deployable SLMs for Cybersecurity CTF Tasks

Context segmentation framework boosts memory-constrained gemma-4 agents on picoCTF, solving 18.52% of tasks standard execution fails, highlighting local SLM offensive risk.

The paper introduces context segmentation, a two-level agentic framework that divides long-horizon CTF exploitation tasks into contextually isolated sub-problems to counter context bloat and cognitive degradation from accumulated tool-call outputs. It evaluates memory-constrained gemma-4 models on the picoCTF dataset; the E4B model achieves competitive rewards with superior token efficiency compared to brute-force retries. It solves 18.52% of tasks that standard agentic execution fails to complete. The work frames locally deployed open-weight SLMs as an escalating risk since they bypass proprietary API guardrails; code is released on GitHub.

arXiv cs.CR · 5d agoAI safety & security1