Listen to this Post
Introduction:
The integration of large language models into vulnerability discovery promises a revolution in application security. However, the first public results from Anthropic’s Mythos AI scanner on the heavily audited curl codebase reveal a sobering reality: five reported issues, four false positives, and only one low‑severity vulnerability – an 80% false positive rate. While this might appear disappointing, security experts argue it is actually a good sign for defenders, indicating that even next‑generation AI models tend to rediscover the same vulnerabilities rather than uncovering novel, weaponizable flaws.
Learning Objectives
- Understand the current limitations and realistic expectations of AI‑powered vulnerability discovery tools.
- Learn practical techniques to reduce false positives using custom fuzzing harnesses, concolic analysis, and orchestrated validation pipelines.
- Implement automated triage and remediation workflows that turn raw AI findings into actionable security outcomes.
You Should Know
- Why 80% False Positives Is Actually Good News for Defenders
The curl project has fixed between 200‑300 bugs over its lifetime, many found by humans and traditional tools. Mythos found only five issues, and after manual review, four were deemed non‑vulnerabilities (three documented API behaviors and one non‑security bug). This suggests that the AI model is not discovering a massive new attack surface – rather, it is converging on the same low‑hanging fruit that defenders already know about. For attackers, the nightmare scenario is an AI that finds one unique, exploitable flaw per run; this result implies that the “golden era of fewer vulnerabilities” might be achievable if all models find the same limited set.
Step‑by‑step guide to evaluate any AI vulnerability scanner on your own codebase:
1. Select a target – Use a well‑audited open‑source project (e.g., OpenSSL, curl, or your internal component).
2. Run the AI scanner – Record every finding (raw output).
3. Manual triage – For each finding, classify: True Positive (valid vuln), False Positive (not a vuln), or Informational (code smell / bug).
4. Calculate FP rate – False Positives / Total Findings.
5. Compare with known CVEs – Check if the true positives were already documented.
Linux command to collect basic repo stats before scanning:
git clone https://github.com/curl/curl.git cd curl cloc . --by-file --csv --out=curl_stats.csv
Windows PowerShell equivalent:
git clone https://github.com/curl/curl.git cd curl (Get-ChildItem -Recurse -Include .c,.h | Measure-Object -Line).Lines
- Building Custom Fuzzing Harnesses to Validate AI Findings
Industry experts (Erik Cabetas, Jonathan Reiter) stress that the real work lies not in bigger foundation models, but in custom harnesses per application architecture. A generic fuzzer or AI model will miss deep logic flaws; a purpose‑built harness that understands state machines, parsers, or cryptographic protocols will yield high‑value results.
Step‑by‑step guide to create a custom fuzzing harness for a URL parser (like curl’s):
1. Identify a target function – e.g., `curl_url_set()` or a protocol parser.
2. Write a harness that calls the function with fuzzed input.
3. Compile the target with sanitizers (ASAN, UBSAN).
4. Use AFL++ or Honggfuzz.
Linux commands for AFL++ on a simple harness:
Install AFL++ sudo apt-get install afl++ Compile target with instrumentation afl-gcc -fsanitize=address -o harness harness.c Run fuzzer with seed inputs afl-fuzz -i seeds/ -o findings/ ./harness @@
Example harness snippet (C):
include <curl/curl.h>
int main(int argc, char argv) {
char data = read_file(argv[bash]); // fuzzed input
CURLU h = curl_url();
curl_url_set(h, CURLUPART_URL, data, 0);
curl_url_cleanup(h);
return 0;
}
Windows (WinAFL + DynamoRIO):
Download WinAFL, then: afl-fuzz.exe -i in -o out -t 5000 -D C:\dynamorio\bin64 -f input.txt -- target.exe @@
3. Orchestrating Static → Dynamic Validation Pipelines
Robert Hawes emphasized that orchestration eliminates chasing ghosts: static AI findings should automatically feed into dynamic analysis to confirm exploitability. A fully autonomous pipeline reduces false positives to zero by providing working Proof‑of‑Concept (PoC) exploits.
Step‑by‑step guide using the open‑source Argus_Scanner (GitHub: dshochat/Argus_Scanner):
1. Clone and configure Argus_Scanner.
- Define a target (e.g., a local web app).
- Run the orchestrator – it performs static analysis, then launches dynamic tests only on flagged paths.
Linux commands:
git clone https://github.com/dshochat/Argus_Scanner.git cd Argus_Scanner pip install -r requirements.txt python argus.py --target http://localhost:8080 --static-scan --dynamic-validation
To simulate an AI finding validation with a custom script:
Assume ai_findings.json from Mythos/Semgrep jq '.vulnerabilities[] | select(.severity == "high")' ai_findings.json > critical.json for issue in $(jq -r '.file' critical.json); do python dynamic_exploit_check.py --file $issue done
4. Reducing False Positives with Concolic Execution
Jonathan Reiter notes that “bespoke concolic analysis” (combining concrete and symbolic execution) is the long pull. Instead of blindly trusting AI, you can use tools like angr to mathematically prove whether a path is exploitable.
Step‑by‑step guide to run concolic analysis on a binary function:
1. Identify a suspicious function (e.g., from AI output).
2. Write a Python script using angr to explore states.
3. Check if user input can reach a dangerous sink (e.g., `memcpy` with tainted size).
Linux install and usage:
pip install angr
Python script to analyze a binary:
import angr
proj = angr.Project("./vuln_binary", auto_load_libs=False)
cfg = proj.analyses.CFGFast()
@simgr.hook(0x400c32) address of vulnerable call
def check_sink(state):
if state.solver.eval(state.regs.rdx > 1024):
print("Exploitable path found!")
state.add_constraints(state.regs.rdx > 1024)
return
5. API Security: Understanding Documented vs. Undocumented Behaviors
Three of Mythos’ false positives were “shortcomings documented in API documentation”. This highlights that AI often lacks context about intended API contracts. Security scanners must distinguish between a genuine flaw and a developer‑aware trade‑off.
Step‑by‑step guide to configure API behavioral testing:
1. Use Swagger/OpenAPI to define expected error responses.
- Run an AI scanner, then filter findings against the API spec.
- Automatically discard any finding that matches documented behavior.
Linux commands using `spectral` and `jq`:
Lint OpenAPI spec spectral lint api.yaml Extract all documented "deprecated" endpoints jq '.paths[].deprecated' api.yaml | grep true > deprecated.txt Run AI scanner and remove those endpoints ai_scanner --target api.example.com | grep -v -f deprecated.txt
Windows (PowerShell) equivalent:
Select-String -Pattern "deprecated" .\api.yaml | ForEach-Object { $_ -replace '.path: (.)','$1' } | Out-File deprecated.txt
ai_scanner.exe --target example.com | Select-String -NotMatch -Pattern (Get-Content deprecated.txt)
6. Cloud Hardening for AI Security Tools
When deploying AI vulnerability scanners in AWS/Azure, isolation and least privilege are critical – the scanner itself becomes a high‑value target.
Step‑by‑step guide to harden a cloud‑based AI scanning pipeline:
1. Run scanners in ephemeral containers (no persistent secrets).
2. Use IAM roles with scoped permissions (no write to production).
3. Encrypt findings at rest and in transit.
4. Implement network controls (VPC, service endpoints).
AWS CLI commands to create a secure scanning Lambda:
Create execution role with minimal perms
aws iam create-role --role-name ScannerRole --assume-role-policy-document file://trust-policy.json
aws iam attach-role-policy --role-name ScannerRole --policy-arn arn:aws:iam::aws:policy/ReadOnlyAccess
Deploy scanner container to ECS with network isolation
aws ecs create-service --cluster scanner-cluster --task-definition ai-scanner --network-configuration "awsvpcConfiguration={subnets=[subnet-abc],securityGroups=[sg-scanner],assignPublicIp=DISABLED}"
Azure CLI:
az container create --resource-group ScannerRG --name aisafescan --image mythos-scanner --vnet scanner-vnet --subnet isolated-subnet
7. Mitigation Strategies: From AI Finding to Patch
Once you identify the one real vulnerability (like curl’s low‑severity issue), you need a repeatable remediation process.
Step‑by‑step guide to triage and fix AI‑discovered vulnerabilities:
- Isolate the vulnerable component in a test environment.
- Write a regression test that triggers the flaw.
3. Apply the patch (or backport).
- Run the AI scanner again to confirm the finding is gone.
Linux commands to bisect and verify a fix:
Clone curl, build with debug symbols git clone https://github.com/curl/curl.git cd curl ./buildconf ./configure --enable-debug make Use gdb to confirm exploit condition gdb --args src/curl --url "vulnerable://payload" After patch, recompile and rerun make clean && make && ./src/curl --url "vulnerable://payload"
What Undercode Say
- False positives are not failure; they are a metric of model convergence. An 80% FP rate on a heavily audited codebase suggests AI is not introducing wild new attack vectors – defenders can focus on the same vulnerabilities attackers have always exploited.
- Custom harnesses > bigger models. The community consensus from Include Security, VulnCheck, and others is that application‑specific fuzzing and concolic analysis will provide far better ROI than simply scaling foundation models.
- Orchestration eliminates the “vapeware” problem. Autonomous pipelines that validate every finding (like Robert Hawes’ product or Argus_Scanner) are the only way to turn AI noise into actionable intelligence.
- API documentation as a security control. Three of Mythos’ false positives were documented behaviors – maintainers should treat API contracts as a filter to reduce noise.
- The golden era of fewer vulnerabilities depends on diversity. If every AI model finds the same bugs, defenders win; if models diverge, attackers gain the asymmetric advantage of finding one unique flaw.
Prediction
Over the next 12–18 months, AI vulnerability scanners will mature from PR‑driven hype to practical orchestration platforms. The winners will not be the models with the lowest false positive claims, but the vendors that offer seamless, validated, and reproducible pipelines – integrating static AI analysis, custom fuzzing, and concolic execution. Expect a backlash against “vapeware” demonstrations, followed by an industry shift toward open benchmarks (like the AIxCC). However, the real disruptive change will occur when AI models begin to design custom harnesses automatically, reducing the human effort currently required to get that single valid finding. Until then, the curl results serve as a healthy reality check: AI is a powerful assistant, not a magic wand.
▶️ Related Video (76% Match):
🎯Let’s Practice For Free:
IT/Security Reporter URL:
Reported By: Isaacevans The – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
