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Fortifying the Future:
Tackling Security Challenges
in AI/ML Applications
All Things Open, October 2023
Christine Abernathy (she/her)
@abernathyca
©2023 F5
2
Have you
tried
ChatGPT?
Artist in residence thanks
to Stable Diffusion :)
Yay Code LLaMa!
Learning Python this
weekend.
Mistral 7B
what!!
Prompt Engineer needed with at
least 5 years experience
©2023 F5
3
What could go wrong…
©2023 F5
4
What could go wrong…
©2023 F5
5
What is Machine Learning?
©2023 F5
6
Train machines to
perform certain tasks
Mimic human actions
and thoughts
Handle any cognitive task at
human+ levels
©2023 F5
7
Supervised Learning
Unsupervised Learning
Reinforcement Learning
©2023 F5
8
Deep Learning
©2023 F5
9
Llama
> Supervised Learning
Deep Learning
©2023 F5
10
What could go wrong…
©2023 F5
11
What could go wrong…
During Model Training After Model Trained After Model Deployed
Spider
“Learning the wrong thing” “Revealing the wrong thing” “Doing the wrong thing”
Integrity Confidentiality Integrity
©2023 F5
12
Reference: https://arxiv.org/pdf/1904.08653.pdf Reference: https://arxiv.org/pdf/1712.09665.pdf
ML Attacks > Adversarial Inputs
Prediction
Noise
Reference: https://arxiv.org/pdf/1707.08945.pdf
©2022 F5
13
• Adversarial Training
• Input Sanitization
• Defensive Distillation
• Gradient Masking / Shielding
Reference: https://arxiv.org/pdf/1904.08653.pdf Reference: https://arxiv.org/pdf/1712.09665.pdf
Prediction
Noise
Reference: https://arxiv.org/pdf/1707.08945.pdf
ML Attacks > Adversarial Inputs
Countermeasures
©2023 F5
14
ML Attacks > Model Inversion
• Limit Model Access
• Regularization
• Differential Privacy
Countermeasures
Prediction:
- Name: Null
- Home: Null
- Confidence: 0
Prediction:
- Name: Baby Yoda
- Home: Coruscant
- Confidence: 0.61
Prediction:
- Name: Baby Yoda
- Home: Coruscant
- Confidence: 0.93
©2023 F5
15
ML Attacks > Membership Inference
• Differential Privacy
• Regularization
• Model Generalization
Countermeasures
Prediction:
- Name: Baby Groot
- Home: Planet X
- Confidence: 0.95
Prediction:
- Name: Baby Yoda
- Home: Coruscant
- Confidence: 0.93
Prediction:
- Name: Mac Jack
- Home: Earth
- Confidence: 0.60
©2023 F5
16
ML Attacks > Model Stealing
• Limit Model Access
• API Protection
• Output Perturbation
Countermeasures
Reference: https://nvd.nist.gov/vuln/detail/CVE-2019-20634
©2023 F5
17
ML Attacks > Data Poisoning
• Data Sanitization
• Outlier Detection
• Model Verification
Countermeasures
©2023 F5
18
ML Attacks > Common Mitigation Strategies
• Monitoring and Logging
• Access Control
• Model Updates
©2023 F5
19
Are we secure now?
©2023 F5
20
Source: https://ml-ops.org/content/end-to-end-ml-workflow
©2023 F5
21
Development
Requirements Design Testing Deployment
Maintenance,
Monitoring
Shift Left
Threat
modeling
Secure coding
Static
analysis
Incidence
response plan
Vulnerability
patches
Risk
assessments
©2023 F5
22
©2023 F5
23
What about Large Language Models?
©2023 F5
24
• Learned language patterns are used to
generate text likely to follow a given
prompt.
• Probabilities of words and phrases are
based on their frequency in the training
data
• LLM’s do not “know” facts and will
hallucinate
• LLM alignment is used to steer the
response to be “appropriate”
Prediction is a core LLM task
What is a Large Language Model (LLM)?
©2023 F5
25
What is open source?
What is open source?
User:
You are a very kind chat
assistant that responds to
user queries.
System:
Open source refers to a
type of software whose
source code is released
under a license…
Assistant:
LLM
Input
Filter
What is open source?
Open source refers to
a type of software …
Output
Filter
LLM Query Flow
Assistant:
©2022 F5
26
LLM Attacks > Prompt Injection
Reference: https://arxiv.org/pdf/2307.15043.pdf
Reference: NVIDIA: Securing LLM Systems Against Prompt Injection
©2022 F5
27
• Privileged Control
• Trust Boundaries
• Dual LLM Pattern
LLM Attacks > Prompt Injection
Countermeasures
Reference: https://arxiv.org/pdf/2307.15043.pdf
©2023 F5
28
©2023 F5
29
Any other security considerations?
©2023 F5
30
©2023 F5
31
Key Takeaways
• Security measures should consider
the entire ML pipeline
• More research + industry
collaboration is needed
• Get involved with communities
working on this
©2023 F5
32
THANK YOU
@abernathyca
©2023 F5
Q & A
33
Fortifying the Future: Tackling Security Challenges in AI/ML Applications

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