Results for “memory-poisoning”

Detect poisoned training data and backdoored models across the ML pipeline using statistical analysis, activation clustering, and spectral signatures.

Audit project and global memory for bloat, stale decisions, duplicates, contradictions, unsafe content, missing provenance, broken routing, and over-budget global files. Load when the user asks to audit memory, clean memory, check memory health, or verify memory quality.

Configures memory protection mechanisms including DEP, ASLR, CFG, and Windows Exploit Protection to harden endpoints against buffer overflows, ROP chains, and code injection.

Exploit web cache mechanisms to serve malicious content to other users by poisoning cached responses through unkeyed headers and parameters during authorized security tests.

Conducts digital forensics investigations on compromised endpoints, including memory acquisition, disk imaging, artifact analysis, and timeline reconstruction for incident response and evidence collection.

Analyzes RAM memory dumps from compromised systems using the Volatility framework to identify malicious processes, injected code, network connections, loaded modules, and extracted credentials.

Conduct authorized security assessments of LLM applications and AI agents, covering prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.