Correlating Account on Ethereum Mixing Service Via Domain-Invariant Feature Learning

The untraceability of transactions facilitated by Ethereum mixing services like Tornado Cash poses significant challenges to blockchain security and financial regulation. Existing methods for correlating mixing accounts suffer from limited labeled data and vulnerability to noisy annotations, whic...

GPML: Graph Processing for Machine Learning

The dramatic increase of complex, multi-step, and rapidly evolving attacks in dynamic networks involves advanced cyber-threat detectors. The GPML Graph Processing for Machine Learning library addresses this need by transforming raw network traffic traces into graph representations, enabling...

Bringing Forensic Readiness to Modern Computer Firmware

Today's computer systems come with a pre-installed tiny operating system, which is also known as UEFI. UEFI has slowly displaced the former legacy PC-BIOS while the main task has not changed: It is responsible for booting the actual operating system. However, features like the network stack make ...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...

SoK: Timeline Based Event Reconstruction for Digital Forensics: Terminology, Methodology, and Current Challenges

Event reconstruction is a technique that examiners can use to attempt to infer past activities by analyzing digital artifacts. Despite its significance, the field suffers from fragmented research, with studies often focusing narrowly on aspects like timeline creation or tampering detection. This...

GRR 3.4.9.1

GRR Rapid Response is an incident response framework focused on remote live forensics. The goal of GRR is to support forensics and investigations in a fast, scalable manner to allow analysts to quickly triage attacks and perform analysis remotely. GRR consists of 2 parts: client and server. GRR...

CVE-2025-32367

The Oz Forensics face recognition application before 4.0.8 late 2023 allows PII retrieval via /statistic/list Insecure Direct Object Reference. NOTE: the number 4.0.8 was used for both the unpatched and patched versions...

CVE-2025-32367

The Oz Forensics face recognition application before 4.0.8 late 2023 allows PII retrieval via /statistic/list Insecure Direct Object Reference. NOTE: the number 4.0.8 was used for both the unpatched and patched versions...

Oz Forensics Oz Liveness 安全漏洞

Oz Forensics Oz Liveness is a leading facial recognition and authentication software from Oz Forensics. A security vulnerability exists in Oz Forensics Oz Liveness versions prior to 4.0.8 late 2023, which stems from an insecure direct object reference that could lead to PII retrieval...

PT-2025-16145 · Unknown · Oz Forensics

Name of the Vulnerable Software and Affected Versions: Oz Forensics face recognition application versions prior to 4.0.8 Description: The issue allows PII retrieval via /statistic/list Insecure Direct Object Reference. Recommendations: For versions prior to 4.0.8, consider disabling access to the...

CVE-2025-32367

CVE-2025-32367 affects the Oz Forensics face recognition application prior to version 4.0.8 (late 2023). The root cause is an Insecure Direct Object Reference flaw in the /statistic/list endpoint, which could allow retrieval of PII. Public references from NVD/Red Hat describe the vulnerability, w...

CVE-2025-32367

The Oz Forensics face recognition application before 4.0.8 late 2023 allows PII retrieval via /statistic/list Insecure Direct Object Reference. NOTE: the number 4.0.8 was used for both the unpatched and patched versions...

CISA Partners with ASD’s ACSC, CCCS, NCSC-UK, and Other International and US Organizations to Release Guidance on Edge Devices

CISA—in partnership with international and U.S. organizations—released guidance to help organizations protect their network edge devices and appliances, such as firewalls, routers, virtual private networks VPN gateways, Internet of Things IoT devices, internet-facing servers, and internet-facing...

Tackling AI threats. Advanced DFIR methods and tools for deepfake detection

TL; DR AI-generated documents, videos and more pose significant challenges for DFIR DFIR teams can harness innovative detection strategies and tooling Digital fingerprinting and watermarking, AI-powered and behavioural analyses Hardware-based forensics and image-specific forensic techniques...

What Graykey Can and Can’t Unlock

This is from 404 Media: The Graykey, a phone unlocking and forensics tool that is used by law enforcement around the world, is only able to retrieve partial data from all modern iPhones that run iOS 18 or iOS 18.0.1, which are two recently released versions of Apple's mobile operating system,...

Kubernetes Audit Log “Gotchas”

How to overcome challenges and security gaps when using K8s audit logs for forensics and attack detection...

Mounting memory with MemProcFS for advanced memory forensics

Mounting memory? This changes everything! TL;DR Memory forensics is crucial for investigations, providing access to volatile data, like running processes and network connections. MemProcFS is a game-changer tool in memory forensics, allowing memory dumps to be mounted and browsed like file system...

Using Volatility for advanced memory forensics

TL;DR Memory forensics enhances investigations by analysing volatile data in RAM unavailable in disk forensics. Key insights from memory include running processes , network connections , encryption keys , and user activity , vital for real-time investigations. Smaller memory images 4-32 GB offer...

CVE-2024-45412

Yeti bridges the gap between CTI and DFIR practitioners by providing a Forensics Intelligence platform and pipeline. Remote user-controlled data tags can reach a Unicode normalization with a compatibility form NFKD. Under Windows, such normalization is costly in resources and may lead to denial o...

CVE-2024-45412 Yeti affected by a Potential Denial of Service due to the One Milion Unicode characters attack

Yeti bridges the gap between CTI and DFIR practitioners by providing a Forensics Intelligence platform and pipeline. Remote user-controlled data tags can reach a Unicode normalization with a compatibility form NFKD. Under Windows, such normalization is costly in resources and may lead to denial o...