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The Reddit community r/netsec is dedicated to discussions around network security, cybersecurity, and related fields. It serves as a platform for professionals, enthusiasts, and learners to share news, updates, tools, and insights about the latest in security. Members post about various topics including vulnerabilities, hacking techniques, data breaches, and security best practices. The subreddit is also a place where users can ask questions, seek advice, or share resources like tutorials and research papers. It's an active forum where discussions can range from technical deep-dives to general industry news. Contributors often include security experts who provide in-depth analysis and advice. The community is strict about keeping discussions on-topic, focusing solely on network security issues. It's also a space where ethical hacking and penetration testing are frequently discussed, with an emphasis on responsible disclosure and legal compliance. The subreddit has a wealth of archived posts and links, making it a valuable resource for anyone interested in cybersecurity. Members are encouraged to stay informed about the latest security trends and threats. Overall, r/netsec is a vital hub for those in the cybersecurity field to stay updated, exchange knowledge, and engage with others who share their interests. The community also promotes career advice and job postings related to cybersecurity.

Thread Of Notes

The concept of time has been on the mind lately, particularly in relation to uptime, pulse checks, execution time, and response time, which are typically viewed as health metrics. These metrics can also be seen as security metrics, as they can provide insight into the security of a system. The idea that timing can be used as a security metric is not new, as it has been used in the past to detect covert timing channels and behavioral baselining. Attackers have long understood that timing can carry information, and researchers have demonstrated timing side-channel attacks against large language models. However, most research has focused on how timing can be exploited, rather than how it can be used to defend against attacks. A recent survey of LLM-agent security identified temporal anomaly detection infrastructure as an open research gap, highlighting the need for more research in this area. The survey noted that current agent deployment frameworks do not support the behavioral baselines needed for temporal anomaly detection, and the discussion has largely focused on session-level behavior. The space between observable events, or the rhythm within a single execution, remains largely unexplored, and it is possible that time is another dimension of observability that has been overlooked. By measuring behavioral changes and entropy across inter-arrival intervals, it may be possible to use timing as a signal rather than noise to improve security. The author is exploring this concept in their own work and is curious to know who else is working on the defensive side of temporal behavior, particularly for agentic systems.
Noma Security has revealed a vulnerability called GitLost targeting GitHub Agentic Workflows. These workflows use AI agents that can access repositories, including private ones, if granted a personal access token. The core of the vulnerability lies in how an attacker can exploit this broad access through a seemingly normal issue on a public repository.GitLost demonstrates indirect prompt injection, shifting the focus from manipulating an agent's output to controlling its actions based on its permissions. The agent, acting as a credentialed entity, can read private repository information that the attacker otherwise cannot access. An example involved the agent reading a private README and posting it as a public comment, serving as the exfiltration channel.GitHub implemented defenses like sandboxing and input cleaning, but Noma found that a simple prefix like "Additionally" bypassed the output scanner. This bypass highlights a fundamental challenge: the difficulty in separating instructions from data in natural language prompts. The issue is structural, as prompt injection is hard to entirely filter out.This vulnerability aligns with the "lethal trifecta" concept: an agent with access to private data, ingesting untrusted content, and possessing an outbound data channel. Noma emphasizes that architectural solutions like isolation and scoped credentials are more effective than pattern-matching defenses. Several other studies, including those by Aikido, Orca, and Invariant Labs, show similar patterns of agents leaking secrets or accessing private data. These findings suggest a broader class of vulnerabilities in current agentic systems.To mitigate these risks, Noma suggests limiting the scope of personal access tokens to single repositories instead of granting organization-wide access. They also recommend restricting what public-facing workflows can post and gating outputs through human review rather than relying solely on threat detection scans. Restricting which authors' content an agent will act upon is another proposed mitigation strategy.