Search: “secret-scanning”

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Mapping out your unknown: A threat hunter’s guide to GitHub

Datadog Security Labs publishes a threat-hunting guide with audit-log queries to detect GitHub token theft, device code phishing, and source code exfiltration.

Datadog's threat-hunting guide covers GitHub audit log queries for detecting compromised accounts, stolen personal access tokens, and malicious OAuth app authorizations. Attackers typically obtain credentials through phishing, credential stuffing, leaked secrets, or device code phishing, then map private repositories, exfiltrate source code, and pivot into connected cloud and CI/CD environments. The guide maps detections to MITRE techniques like T1078 and T1528 and documents GitHub logging quirks affecting attribution, token metadata, and visibility fields.

Datadog Security Labs·Sep 16, 00:00 UTC · 1d agoResearch in the wild1

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New AI Attack Hides Malicious Instructions in Normal-Looking Text to Evade Safety Filters

Check Point researchers show crafted prose hides policy-violating instructions that bypass all tested LLM gatekeepers, including GPT-4o mini and Llama Guard 3.

A new prompt-crafting technique embeds malicious payloads inside grammatical, natural-looking text without Base64, invisible Unicode, or obvious encodings, defeating lightweight pre-screening gatekeepers. In testing, all four evaluated gatekeeper models—gpt-4o-mini-2024-07-18, gpt-oss-safeguard:20b, claude-3-haiku-20240307, and llama-guard3:8b—classified the crafted wrappers as safe at a 100% bypass rate across 23 obfuscated prompts. GPT-5 Thinking in high-reasoning mode recovered and acted on the hidden instruction in 17 of 18 tests (~94.4%), often spending over a minute and multiple Python executions. Researchers recommend paraphrasing untrusted input, hardening gatekeeper policies, and applying defense-in-depth controls for agentic deployments.

GBHackers·Sep 11, 05:53 UTC · 5d agoAI safety & security 2 sources

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Rare Not Random Using Token Efficiency for Secrets Scanning

Researcher proposes token efficiency (string length divided by BPE token count) as a better post-regex filter than entropy for secrets scanning, validated on CredData.

The post explores whether Byte-Pair Encoding tokenization can replace Shannon entropy as the primary filter for candidate secrets captured by regex in tools like Gitleaks. It defines 'token efficiency' as string length divided by token count under the cl100k_base tokenizer; secret-like strings such as GitHub tokens tokenize into many small tokens and score low, while natural text scores high. Evaluating labeled secrets from the CredData dataset shows a usable separation, with roughly 2.5 suggested as a minimum cutoff versus Gitleaks' 3.5 entropy threshold. The technique is positioned as a post-regex filtering step rather than a standalone detector.

Lobsters · security·Sep 12, 06:42 UTC · 4d agoResearch

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Microsoft Copilot reveals secret input that allowed it to be hacked

Microsoft disclosed a hidden input in Copilot that let attackers steal passwords from users who clicked a crafted link.

Microsoft revealed that Copilot contained a secret, undocumented input parameter that allowed the assistant to be compromised. Attackers could abuse the hidden input to steal passwords when a target clicked a malicious link. The disclosure highlights hidden-parameter risks in widely deployed AI assistants.

Ars Technica · Security·Aug 18, 13:00 UTC · 29d agoAI safety & security in the wild1

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Open-source secrets scanning tool Sift hunts credentials in Microsoft 365, Slack, and Jira

Stratus Security open-sourced Sift, a CLI secrets scanner covering Active Directory, SharePoint, Teams, Slack, Jira, and Confluence, outperforming Snaffler in benchmarks.

Stratus Security released Sift, a free open-source command line tool that hunts passwords, API keys, and sensitive data across local disks, Windows shares, Active Directory, SharePoint, OneDrive, Teams, Slack, Jira, and Confluence. In the firm's benchmarks, Sift scanned 250,000 files in 10.61 seconds versus Snaffler's 25.48 and averaged 92 MiB memory versus Snaffler's 337 MiB. Optional false-positive filtering runs through a local language model via Ollama, and scans write checkpoints so interrupted runs resume. The tool found thousands of credentials in Jira ticket comments that prior pentesting had missed.

Help Net Security·Sep 2, 00:00 UTC · 15d agoTools

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SEEK: Secure and Efficient Encrypted Keyword Search For Privacy-Preserving Messaging Protocols

Researchers propose SEEK, a homomorphic-encryption plus 2PC protocol for encrypted keyword search that hides keywords while detecting matches.

SEEK partitions messages into ciphertext fragments with minimum sufficient overlap and homomorphically correlates them using encrypted keyword trapdoors, combined with 2PC-based selected decoding, blinded zero testing, and secure aggregation. It reduces sender-side encryption and upload overhead by up to two orders of magnitude over state-of-the-art baselines and computes correlations up to 5.47x faster, revealing only the keyword presence bit while hiding contents, counts, and locations. A prototype achieves 1.92 seconds online computation per search on a weekly messaging history and is realized as a web and cross-platform mobile application.

arXiv cs.CR·Sep 16, 10:54 UTC · 18h agoResearch

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The Coding-Agent Trap: When a "Free" LLM Endpoint Is the Adversary, (Mon, Aug 31st)

A SANS honeypot caught a real coding-agent session routed to a rogue "free" LLM endpoint, exposing a Windows user's transcript and tool outputs.

A SANS analyst describes how an internet-exposed inference honeypot was discovered, relabeled with sought-after model names like DeepSeek, and enrolled in infrastructure serving "free" LLM backends. On 2026-08-30 an opencode terminal coding agent sent an 88-message, 224 KB transcript 210 times in 91 seconds via a China Unicom relay, exposing directory listings, tool outputs and read file portions. The analyst frames tool-enabled agents treating model endpoints as trusted control planes as a novel risk — a "rogue model endpoint" that could request tool executions on the user's machine.

SANS Internet Storm Center·Aug 31, 20:00 UTC · 16d agoAI safety & security1

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Stealing AI Reasoning Traces

Researchers demonstrate a decryption jailbreak that extracts encrypted reasoning traces from Anthropic, OpenAI, and Google LLM APIs via weaker sibling models.

The paper exploits the fact that encrypted chain-of-thought blocks returned by LLM providers are interchangeable across sessions, users, and models within a provider's ecosystem. Injecting an encrypted trace into a weaker, less-safeguarded model from the same provider forces it to output the trace in plaintext, bypassing anti-distillation mechanisms. Decoding 315,320 reasoning blocks scraped from public repositories recovered 367 PII artifacts and 182 credentials, showing large-scale private data leakage. The flaw also enables hidden hazardous information disclosure and invisible prompt injections embedded in encrypted blocks; mitigations were proposed after responsible disclosure.

Schneier on Security·Sep 8, 10:20 UTC · 8d agoAI safety & security

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Grok exfiltrates user data when malicious instructions are encrypted

Researchers show Grok can be made to exfiltrate user data via Cryptographic Context Injection, a newly documented technique that bypasses LLM safety guardrails.

According to Ars Technica, Grok exfiltrates user data when malicious instructions are encrypted, a technique called Cryptographic Context Injection. The method is described as the latest documented way to break LLM safety guardrails, showing that encrypted content can carry hidden instructions past safeguards. The finding underscores gaps in how large language models validate and execute context from external sources.

Ars Technica · Security·Aug 20, 13:00 UTC · 27d agoAI safety & security1

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Rethinking Indirect Prompt Injection as a Test-Time Search Problem

Researchers frame indirect prompt injection as test-time search, showing added attacker compute improves vulnerability discovery and exploitation against tool-using agents.

The paper models indirect prompt injection as a test-time search over a task-dependent attack surface shaped by the environment, user task, and injection goal. The authors build an agentic attacker with a dedicated search harness that performs reconnaissance, structured strategy reasoning, and adaptive evaluation using victim-agent feedback. Experiments show more attacker test-time compute improves discovery and exploitation of injection vulnerabilities, with explicit strategy management needed to avoid redundant search. The results argue that agentic security evaluations should characterize attacker search procedures and compute budgets rather than treating attack success as budget-independent.

arXiv cs.CR·Sep 3, 21:28 UTC · 13d agoAI safety & security

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Hackers Can Hide Malicious AI Commands Inside Normal English to Bypass Security Filters

Check Point's PuzzleMask technique hides malicious prompts in ordinary English that fast gatekeeper models miss but high-reasoning downstream models execute.

Check Point researchers disclosed PuzzleMask, a technique concealing policy-breaking instructions in natural-language prose without encodings or invisible characters. Fast screening models classified all 23 crafted wrappers as safe, while a high-reasoning model recovered and acted on the hidden instruction in 17 of 18 tests (94.4%). The gap stems from capability imbalance between gatekeeper and target models, with defenses including paraphrasing untrusted input, stricter self-referential wording rules, and output/tool-call monitoring.

Cyber Security News·Sep 11, 07:36 UTC · 5d agoAI safety & security1

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Signal's new security feature checks if your encrypted chats were tampered with

Signal adds automatic key verification, a key-transparency system audited by Cloudflare and Trail of Bits that detects key-directory tampering in E2EE chats.

Signal launched automatic key verification, a key-transparency system that confirms a phone number or username remains bound to a consistent encryption key across Signal's key directory. Cloudflare and Trail of Bits independently audit the system, with cryptographic protection preventing auditors from seeing users' identifiers in plaintext. The feature mitigates key substitution by a compromised directory or privileged insider, though it currently requires the contact's phone number and users can fall back to manual Safety Number verification.

Help Net Security·Aug 12, 00:00 UTC · Aug 12, 2026Tools

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A Detection Engineer's Guide for Delegating Work to AI

Huntress argues detection engineers should only delegate security work to AI when outputs can be independently verified.

A Huntress detection engineer argues that the deciding factor for handing tasks to AI is whether the output can be checked, not whether the model is trusted. The piece frames human verification as the gate for delegating security engineering work to AI assistants. It is guidance/opinion aimed at defenders building detections with AI help.

Huntress·Aug 19, 04:00 UTC · 29d agoAI safety & security

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Hackers Weaponize Agentic AI to Automate Reconnaissance, Exploitation and Post-Exploitation

Google GTIG reports threat actors using agentic AI to automate reconnaissance, exploit selection, and credential harvesting, compromising thousands of secrets.

Google Threat Intelligence Group's Q3 2026 AI Threat Tracker documents threat actors operationalizing agentic AI: in one Mandiant investigation, a financially motivated actor built and executed a credential-harvesting operation in under six hours, with an exposed 'Recon' framework managing more than 23,800 harvested secrets including cloud and AI-service API keys. A Chinese-speaking actor tracked as knaithe used a DeepSeek-powered Hermes Agent for automated reconnaissance and vulnerability enumeration, pivoting from Langflow to n8n and enabling manual exploitation of exposed Citrix NetScaler, Marimo, Apache Tomcat, and VPN infrastructure. Operators harvested Citrix session cookies from process memory to bypass MFA, obtained AWS credentials from compromised Marimo instances, and deployed the Go-based NKAbuse backdoor, with reported RCE and data exfiltration. Google notes fully autonomous end-to-end AI attack pipelines have not yet been observed in the wild.

GBHackers·Sep 9, 09:12 UTC · 7d agoThreat actor in the wild2

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Threat Actors Are Posing as OpenAI, Anthropic and DeepSeek to Target Credentials and Secrets

GreyNoise observes scanners spoofing OpenAI, Anthropic, and DeepSeek crawler user agents to hunt leaked credentials and secrets.

GreyNoise is observing automated scanners that pose as the web crawlers of OpenAI, Anthropic, DeepSeek, and Fortune 500 companies. The scanners use forged user agents while requesting files where misconfigured web servers frequently leak secrets and credentials. This activity abuses trust in AI crawler traffic to discover exposed credentials for follow-on attacks.

GreyNoise·Aug 28, 00:00 UTC · 20d agoExploit / PoC in the wild

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Hackers Deploy Agentic AI to Automate Exploitation and Mass Credential Harvesting

Google GTIG documents a financially motivated actor using a multi-agent AI framework to automate credential harvesting, compromising over 23,800 secrets within hours.

Google Threat Intelligence Group (GTIG) documented a financially motivated actor that compromised an unnamed organization's cloud infrastructure and used a multi-agent AI framework to automate vulnerability scanning, credential harvesting, troubleshooting, and IP rotation. The operation went from planning to mass credential compromise in under six hours, harvesting more than 23,800 secrets including cloud and AI-service API keys via an exposed C2 dashboard called 'Recon'. The actor directed specialized agents using an AI coding chatbot and Markdown instruction files such as AGENTS.KNOWLEDGE.md and agentic_vuln_research.md. Google has not observed fully autonomous zero-day exploitation; the shift automates labor-intensive tasks like reconnaissance, account validation, and infrastructure management, sharply shrinking detection windows.

GBHackers·Sep 15, 07:46 UTC · 1d agoThreat actor in the wild1

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Your Critical Vulnerabilities Might Not Be Your Biggest Risk

Autonomous penetration testing advocates prioritize exploitable attack paths over raw vulnerability severity for continuous security validation.

The article argues that scanner severity scores lack context: a critical flaw behind strong segmentation may be low priority, while a medium flaw on internet-facing systems can provide a foothold chained toward sensitive data. It positions autonomous penetration testing and attack path validation as the execution layer for continuous security validation, replacing point-in-time assessments. The piece is vendor-authored thought leadership rather than incident or vulnerability news.

The Hacker News·Sep 11, 11:30 UTC · 5d agoIndustry1

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SpecGuard: Inference-Time Backdoor Detection For Free

SpecGuard detects backdoored LLM behavior at inference time using speculative decoding acceptance rates, adding no extra model computation.

Researchers propose SpecGuard, an inference-time backdoor detector that repurposes draft-token acceptance rates from speculative decoding as a detection signal at zero added model-computation cost. When a trigger shifts the target model toward attacker-controlled behavior, the clean draft model's acceptance rate changes, exposing the backdoor; the paper formalizes when this signal appears and shows suppressing it weakens the backdoor. Experiments across diverse backdoor types and model families show reliable detection, including stealthy cases invisible to input-level filters. Speculative decoding is positioned as a free, always-on monitor for frequently updated deployed models.

arXiv cs.CR·Sep 10, 16:51 UTC · 6d agoAI safety & security 2 sources2

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PentestGPT: Open-source automated penetration testing agentic framework

Open-source PentestGPT runs autonomous LLM-driven penetration tests via Claude Code and Codex, with legacy human-in-the-loop mode supporting many providers.

PentestGPT, originally published at USENIX Security 2024 by Gelei Deng and colleagues, is an open-source framework that lets a large language model autonomously run penetration testing stages (recon, exploit, walkthrough) with no human in the loop, driving Claude Code or Codex CLIs. A legacy interactive mode uses three cooperating LLM sessions maintaining a Pentesting Task Tree and supports OpenAI, Anthropic, Google Gemini, DeepSeek, xAI, Qwen, Moonshot, and local models via Ollama. The tool sends anonymous telemetry to Langfuse by default, excluding command outputs, credentials, and flags, and is available free on GitHub.

Help Net Security·Aug 12, 00:00 UTC · Aug 12, 2026Tools1

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gr-PHYSEC: Real-time Channel-based Key Generation for Physical Layer Secure Wireless Communications

gr-PHYSEC GNU Radio module derives symmetric encryption keys from wireless channel randomness using a neural network, validated on robotic platforms with ADALM-Pluto SDRs.

The paper introduces gr-PHYSEC, a GNU Radio out-of-tree module for real-time physical-layer key generation that derives symmetric keys from the wireless channel's inherent randomness instead of pre-shared secrets. A trained neural network extracts channel features between trusted parties during probe exchanges; features are quantized into binary keys, reconciled via Reed-Solomon encoding, and secured with SHA-512 hashing before direct use for encryption. Real-world experiments at the FAU CAAI connected robotics testbed using ADALM Pluto software-defined radios and NVIDIA Jetson Orin demonstrated low key disagreement rates and NIST-verified randomness. Source code is publicly available on GitHub.

arXiv cs.CR·Sep 14, 21:37 UTC · 2d agoResearch

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Credential Theft: How Attackers Steal & Use Stolen Credentials

Huntress explains how attackers steal credentials through phishing, AitM, infostealers, and dumping, then use them for lateral movement, BEC, and ransomware.

Huntress published an educational overview of credential theft, citing that roughly 70% of confirmed data breaches begin with stolen credentials. It details acquisition methods including phishing, adversary-in-the-middle attacks that capture MFA session tokens, infostealers (nearly a quarter of threats Huntress observed in 2025), Mimikatz-based credential dumping, credential stuffing, and password spraying. The piece then covers post-theft actions such as lateral movement, privilege escalation, account takeover, business email compromise, and ransomware, and closes with behavioral detection guidance and layered prevention strategies.

Huntress·Sep 10, 16:00 UTC · 6d agoResearch

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LLM-Based Penetration Testing in the Presence of Honeypots

Studies honeypot-aware budget allocation for LLM attack agents, showing detector-guided policies let agents skip deception and compromise real hosts efficiently.

The paper formalizes LLM attacker behavior against honeypots as a budgeted decision process, where agents choose to continue or skip targets when honeypot suspicion arises. A detector-guided policy lets LLM agents allocate execution budget effectively across a mixed host pool in a controlled testbed. Findings show LLM-driven attackers can reason about heterogeneous artifacts and use honeypot suspicion to guide target selection, challenging traditional deception defenses that rely on realism and obscurity against human or script-driven attackers.

arXiv cs.CR·Sep 8, 01:09 UTC · 9d agoResearch

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Post-DEF CON Phishing Uses Malicious Google Doc to Deliver Malware

Huntress uncovered post-DEF CON phishing via X direct messages using a malicious Google Doc to deliver AMOS and NetSupport RAT malware.

Huntress uncovered a phishing campaign targeting attendees after Black Hat and DEF CON. Attackers used X direct messages pointing to a malicious Google Doc as the delivery vehicle. Payloads include AMOS, a macOS infostealer, and the NetSupport RAT, among other malware.

Huntress·Aug 19, 13:00 UTC · 28d agoPhishing & fraud

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Seeing is Not Believing: Breaking the Physical-to-Digital Trust Boundary in Robotics

Researchers show a single ROS 2 environment variable lets attackers inject fake telemetry and hijack robots while spoofing downstream remote attestation.

A pre-built hook loaded via one modified environment variable covertly intercepts and injects both telemetry and control signals before publication in ROS 2, breaking the physical-to-digital trust boundary in multi-robot task handovers. Attackers can also distribute compromised third-party Docker containers and auxiliary tools embedding the hooks. On a physical Franka Emika arm running Secure ROS 2, the attack injects fabricated telemetry in real time with roughly 3 ms jitter and achieved an 87% success rate even against an AI-based detector. Findings were responsibly disclosed to the ROS 2 development team.

arXiv cs.CR·Sep 8, 05:50 UTC · 8d agoVulnerability1

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SpiderSilk Hunts External Threats With AI-Based Scanner

Dubai-based startup SpiderSilk offers an AI-based scanner that surveys billions of IP addresses to find exposed assets, leaked data, and zero-day vulnerabilities.

SpiderSilk, a threat detection startup based in Dubai, uses artificial intelligence tools to scan billions of IP addresses. The scanner identifies exposed assets, leaked data, and zero-day vulnerabilities from an external perspective. The approach targets external attack surface discovery for defenders.

Dark Reading·Sep 11, 18:27 UTC · 5d agoTools

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Threats Making WAVs - Incident Response to a Cryptomining Attack

Guardicore researchers dissect a cryptomining attack that hid a cryptominer inside WAV files, mapping the full infection chain and response steps.

Guardicore security researchers present a full analysis of a cryptomining attack that concealed a cryptominer inside WAV audio files. The report documents the complete attack chain from detection through infection, network propagation, and malware analysis. It also includes recommendations for optimizing incident response processes in data centers.

Akamai Blog·Sep 8, 15:13 UTC · 8d agoMalware in the wild

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