Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oyi77/1ai-skills --skill analyzing-heap-spray-exploitationgit clone --depth 1 https://github.com/oyi77/1ai-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-heap-spray-exploitation)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-heap-spray-exploitation"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-heap-spray-exploitation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oyi77/1ai-skills/analyzing-heap-spray-exploitation"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/analyzing-heap-spray-exploitation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00061 | $0.01174 |
| Opus 5 | $0.00030 | $0.00587 |
| Sonnet 5 | $0.00012 | $0.00235 |
| Haiku 4.5 | $0.00006 | $0.00117 |
Grade A, and why
analyzing-heap-spray-exploitation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 126 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyzing Heap Spray Exploitation
Overview
Heap spraying is an exploitation technique that fills large regions of a process's heap with attacker-controlled data (typically NOP sleds followed by shellcode) to increase the reliability of code execution exploits. This skill covers detecting heap spray artifacts in memory dumps using Volatility3's malfind, vadinfo, and memmap plugins, identifying suspicious contiguous memory allocations, scanning for NOP sled patterns (0x90, 0x0c0c0c0c), and extracting embedded shellcode for analysis.
When to Use
Trigger phrases:
-
"analyzing heap spray exploitation"
-
"Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins "
-
When investigating security incidents that require analyzing heap spray exploitation
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When building detection rules or threat hunting queries for this domain
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When SOC analysts need structured procedures for this analysis type
-
When validating security monitoring coverage for related attack techniques
Prerequisites
- Python 3.9+ with
volatility3framework installed - Memory dump file (.raw, .vmem, .dmp format)
- Understanding of virtual memory layout and VAD (Virtual Address Descriptor) trees
- Familiarity with common shellcode patterns and NOP sled encodings
Steps
# Example: IOC detection
import re
IOC_PATTERNS = {
"ip": r"\b(?:\d{1,3}\.){3}\d{1,3}\b",
"domain": r"\b[a-z0-9-]+\.[a-z]{2,}\b",
"hash_md5": r"\b[a-f0-9]{32}\b",
"hash_sha256": r"\b[a-f0-9]{64}\b",
}
def extract_iocs(text: str) -> dict:
return {k: re.findall(v, text) for k, v in IOC_PATTERNS.items()}
- Scope and authorize — confirm written authorization and define target boundaries
- Reconnaissance — enumerate targets, services, and potential attack surfaces
- Exploitation — attempt exploitation of identified vulnerabilities within scope
- Post-exploitation — document access level, lateral movement, and data exposure
- Report and remediate — compile findings with reproduction steps and fix recommendations
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 126 lines · 61 tokens per session scan A 2b07a81405fc
analyzing-heap-spray-exploitation is a skill published in the GitHub repository oyi77/1ai-skills (12 stars, last pushed yesterday), licensed MIT. It adds 61 tokens to every session and 1,174 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
Other skills, from other repositories
analyzing-heap-spray-exploitation
Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.
analyzing-heap-spray-exploitation
Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.
analyzing-heap-spray-exploitation
Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.
analyzing-heap-spray-exploitation
Detect and analyze heap spray attacks in memory dumps using Volatility3 plugins to identify NOP sled patterns, shellcode landing zones, and suspicious large allocations in process virtual address space.
analyzing-linux-kernel-rootkits
Detect kernel-level rootkits in Linux memory dumps using Volatility3 linux plugins (checksyscall, lsmod, hiddenmodules), rkhunter system scanning, and /proc vs /sys discrepancy analysis to identify hooked syscalls, hidden kernel modules, and tampered system structures.
analyzing-heap-spray-exploitation
A security-analysis skill for finding possible heap-spray attacks in a computer’s memory dump. A heap-spray attack fills large memory areas with repeated data to increase the chance that harmful code runs through a software vulnerability.